A positive feedback circuit comprising p21 and HIF-1α aggravates hypoxia-induced radioresistance of glioblastoma by promoting Glut1/LDHA-mediated glycolysis.

The radioresistance induced by hypoxia is the major obstacle in the successful treatment of cancer radiotherapy. p21 was initially identified as a widespread inhibitor of cyclin-dependent kinases, through which mediates the p53-dependent cell cycle G1 phase arrest in response to a variety of stress stimuli. In this study, we discovered a novel function of p21, which participated in the regulation of metabolic pathways under hypoxia. We found that p21 was upregulated in glioblastoma (GBM) cells under hypoxic conditions, which enhanced the radioresistance of GBM cells. In principle, HIF-1α is bound directly to the hypoxia response elements (HREs) of the p21 promoter to enhance its transcription activity, in turn, p21 also promoted the transcription of HIF-1α at the mRNA level and maintained HIF-1α function under oxygen deficiency. The positive correlation between p21 and HIF-1α augmented Glut1/LDHA-mediated glycolysis and aggravated the radioresistance of GBM cells. Thus, our results constructed a positive feedback circuit comprising p21/HIF-1α that might play a key role in enhancing the radioresistance of GBM under hypoxia.

Design, synthesis and evaluate of novel dual FGFR1 and HDAC inhibitors bearing an indazole scaffold.

Both histone deacetylase (HDAC) and fibroblast growth factor receptor (FGFR) are important targets for cancer therapy. Although combining dual HDAC pharmacophore with tyrosine kinase inhibitors (TKIs) had achieved a successful progress, dual HDAC/FGFR1 inhibitors haven't been reported yet. Herein, we designed a series of hybrids bearing 1H-indazol-3-amine and benzohydroxamic acids scaffold with scaffold hopping and molecular hybridization strategies. Among them, compound 7j showed the most potent inhibitory activity against HDAC6 with IC50 of 34 nM and exhibited the great inhibitory activities against a human breast cancer cell line MCF-7 with IC50 of 9 µM in vitro. Meanwhile, the compound also exhibited moderate FGFR1 inhibitory activities. This study provides new tool compounds for further exploration of dual HDAC/FGFR1 inhibition.

A meta-analysis evaluates the efficacy of intravenous acetaminophen for pain management in knee or hip arthroplasty.

BACKGROUND: The objective of this study was to assess whether intravenous acetaminophen for patients undergoing knee or hip arthroplasty could reduce the opioid consumption and improve pain management. METHOD: Eligible studies were searched from electronic databases including PubMed, Web of Science, Embase (Ovid interface) and Cochrane Library (Ovid interface). The quality assessments were performed according to the Cochrane systematic review method. The assessed outcomes were including opioid consumption, pain scores, length of hospital stays and total occurrence of adverse events. RESULTS: Among 832 records identified, six randomized controlled trials (RCTs) and five non-RCTs were eligible for data extraction and meta-analysis. According to the outcomes, the patients receive intravenous acetaminophen had less total opioid consumption after knee or hip artroplasty (SMD = -0.66; 95%CI, -1.13 to -0.20), but they did not obtain statistical improvement of postoperative pain control at postoperative day 0 (POD0, SMD = -0,15; 95%CI, -0.36 to 0.07), POD1(SMD = 0,12; 95%CI, -0.13 to 0.36), POD2 (SMD = -0,29; 95%CI, -0.70 to 0.12) and POD3 (SMD = -0,04; 95%CI, -0.49 to 0.41). Meanwhile, there were similar outcomes about the length of hospital stays in patients whether or not receiving intravenous acetaminophen (SMD = -0,05; 95%CI, -0.26 to 0.15). And, the total adverse effects occurrence also didn't show any significant difference between the acetaminophen group and control group (OR = 0.87; 95%CI, 0.57 to 1.33). CONCLUSIONS: Perioperative intravenous acetaminophen use in multimodal analgesia could significantly reduce of total opioid consumption, but it did not contribute to decrease the average pain scores and shorten the length of hospital stays in total hip or knee arthroplasty.

The Liver Protection Effects of Maltol, a Flavoring Agent, on Carbon Tetrachloride-Induced Acute Liver Injury in Mice via Inhibiting Apoptosis and Inflammatory Response.

The purpose of this research was to evaluate whether maltol could protect from hepatic injury induced by carbon tetrachloride (CCl4) in vivo by inhibition of apoptosis and inflammatory responses. In this work, maltol was administered at a level of 100 mg/kg for 15 days prior to exposure to a single injection of CCl4 (0.25%, i.p.). The results clearly indicated that the intrapulmonary injection of CCl4 resulted in a sharp increase in serum aspartate transaminase (AST) and alanine transaminase (ALT) activities, tumor necrosis factor-α (TNF-α), irreducible nitric oxide synthase (iNOS), nuclear factor-kappa B (NF-κB) and interleukin-1ß (IL-1ß) levels. Histopathological examination demonstrated severe hepatocyte necrosis and the destruction of architecture in liver lesions. Immunohistochemical staining and western blot analysis suggested an accumulation of iNOS, NF-κB, IL-1ß and TNF-α expression. Maltol, when administered to mice for 15 days, can significantly improve these deleterious changes. In addition, TUNEL and Hoechst 33258 staining showed that a liver cell nucleus of a model group diffused uniform fluorescence following CCl4 injection. Maltol pretreatment groups did not show significant cell nuclear condensation and fragmentation, indicating that maltol inhibited CCl4-induced cell apoptosis. By evaluating the liver catalase (CAT), glutathione (GSH), superoxide dismutase (SOD) activity, and further using a single agent to evaluate the oxidative stress in CCl4-induced hepatotoxicity by immunofluorescence staining, maltol dramatically attenuated the reduction levels of hepatic CAT, GSH and SOD, and the over-expression levels of CYP2E1 and HO-1. In the mouse model of CCl4-induced liver injury, we have demonstrated that the inflammatory responses were inhibited, the serum levels of ALT and AST were reduced, cell apoptosis was suppressed, and liver injury caused by CCl4 was alleviated by maltol, demonstrating that maltol may be an efficient hepatoprotective agent.

Design, synthesis and evaluation of antitumor activity of new rotundic acid acylhydrazone derivatives.

In light of the important antitumor activity of acylhydrazone compounds and based on our previous study, 18 new rotundic acid (RA) acylhydrazone derivatives were synthesized. All of the compounds were characterized by their spectroscopic data. The antiproliferative activity of the compounds was evaluated in vitro via the MTT method in three tumor cell lines, including A-375 (human malignant melanoma cells), SPC-A1 (human lung adenocarcinoma) and NCI-H446 (small cell lung cancer). The results showed that the antiproliferative activity of all of the compounds on the NCI-H446 cell line did not increase compared to RA, however, most of the derivatives exhibited higher activity against the A375 and SPC-A1 cell lines as compared to RA. Importantly, the antiproliferative activities of compounds 5a and 5b were the highest among the compounds, with IC(50) values <10 µM. Collectively, compounds 5a and 5b may act as potential anti-tumor agents in the future.

[HPLC Fingerprint and Determination in the Root of Amorpha fruticosa].

Objective: To establish HPLC fingerprint in the root of Amorpha fruticosa, and simultaneously to determine the content of calycosin-7-O-ß-D-glucopyranoside, ononin, calycosin, formononetin. Methods: The analytical column was Diamonsil C18( 250 mm ×4. 6 mm,5 µm). The mobile phase was acetonitrile( A)-water( B)( containing 0. 2% phosphoric acid) in gradient elution, and the detection wavelength was set at 260 nm. "Chromatographic fingerprint similarity evaluation software "version( 2004A) was used to evaluate similarity for the ten batches medicinal materials,and SPSS software was used for cluster analysis. Results: The HPLC fingerprint of the root of Amorpha fruticosa was established with good separation, and four chemical compositions were determinated. 16 common peaks were defined in the HPLC fingerprint among the 10 batches of the root of Amorpa fruticosa. The similarity among them was more than0. 90. Conclusion: This analytical method has strong features,with a good repeatability and the method is simple, which can be used efficiently in the quality control in the root of Amorpha fruticosa.

Multi-omics reveals the role of ENO1 in bladder cancer and constructs an epithelial-related prognostic model to predict prognosis and efficacy.

α-Enolase (ENO1) is a crucial molecular target for tumor therapy and has emerged as a research hotspot in recent decades. Here, we aimed to explore the role of ENO1 in bladder cancer (BLCA) and then construct a signature to predict the prognosis and treatment response of BLCA. Firstly, we found ENO1 was highly expressed in BLCA tissues, as verified by IHC, and was associated with poor prognosis. The analysis of the tumor immune microenvironment by bulk RNA-seq and scRNA-seq showed that ENO1 was associated with CD8+ T-cell exhaustion. Additionally, the results in vitro showed that ENO1 could promote the proliferation and invasion of BLCA cells. Then, the analysis of epithelial cells (ECs) revealed that ENO1 might promote BLCA progression by metabolism, the cell cycle and some carcinogenic pathways. A total of 249 hub genes were obtained from differentially expressed genes between ENO1-related ECs, and we used LASSO analysis to construct a novel signature that not only accurately predicted the prognosis of BLCA patients but also predicted the response to treatment for BLCA. Finally, we constructed a nomogram to better guide clinical application. In conclusion, through multi-omics analysis, we found that ENO1 was overexpressed in bladder cancer and associated with poor prognosis, CD8+ T-cell exhaustion and epithelial heterogeneity. Moreover, the prognosis and treatment of patients can be well predicted by constructing an epithelial-related prognostic signature.

Coupled scRNA-seq and Bulk-seq reveal the role of HMMR in hepatocellular carcinoma.

Background: Hyaluronan-mediated motility receptor (HMMR) is overexpressed in multiple carcinomas and influences the development and treatment of several cancers. However, its role in hepatocellular carcinoma (HCC) remains unclear. Methods: The "limma" and "GSVA" packages in R were used to perform differential expression analysis and to assess the activity of signalling pathways, respectively. InferCNV was used to infer copy number variation (CNV) for each hepatocyte and "CellChat" was used to analyse intercellular communication networks. Recursive partitioning analysis (RPA) was used to re-stage HCC patients. The IC50 values of various drugs were evaluated using the "pRRophetic" package. In addition, quantitative reverse transcription polymerase chain reaction (qRT-PCR) was performed to confirm HMMR expression in an HCC tissue microarray. Flow cytometry (FCM) and cloning, Edu and wound healing assays were used to explore the capacity of HMMR to regulate HCC tumour. Results: Multiple cohort studies and qRT-PCR demonstrated that HMMR was overexpressed in HCC tissue compared with normal tissue. In addition, HMMR had excellent diagnostic performance. HMMR knockdown inhibited the proliferation and migration of HCC cells in vitro. Moreover, high HMMR expression was associated with "G2M checkpoint" and "E2F targets" in bulk RNA and scRNA-seq, and FCM confirmed that HMMR could regulate the cell cycle. In addition, HMMR was involved in the regulation of the tumour immune microenvironment via immune cell infiltration and intercellular interactions. Furthermore, HMMR was positively associated with genomic heterogeneity with patients with high HMMR expression potentially benefitting more from immunotherapy. Moreover, HMMR was associated with poor prognosis in patients with HCC and the re-staging by recursive partitioning analysis (RPA) gave a good prognosis prediction value and could guide chemotherapy and targeted therapy. Conclusion: The results of the present study show that HMMR could play a role in the diagnosis, prognosis, and treatments of patients with HCC based on bulk RNA-seq and scRAN-seq analyses and is a promising molecular marker for HCC.

Insight into uniform filming of LiF-rich interphase via synergistic adsorption for high-performance lithium metal anode.

Multi-scale simulation is an important basis for constructing digital batteries to improve battery design and application. LiF-rich solid electrolyte interphase (SEI) is experimentally proven to be crucial for the electrochemical performance of lithium metal batteries. However, the LiF-rich SEI is sensitive to various electrolyte formulas and the fundamental mechanism is still unclear. Herein, the structure and formation mechanism of LiF-rich SEI in different electrolyte formulas have been reviewed. On this basis, it further discussed the possible filming mechanism of LiF-rich SEI determined by the initial adsorption of the electrolyte-derived species on the lithium metal anode (LMA). It proposed that individual LiF species follow the Volmer-Weber mode of film growth due to its poor wettability on LMA. Whereas, the synergistic adsorption of additive-derived species with LiF promotes the Frank-Vander Merwe mode of film growth, resulting in uniform LiF deposition on the LMA surface. This perspective provides new insight into the correlation between high LiF content, wettability of LiF, and highperformance of uniform LiF-rich SEI. It disclosed the importance of additive assistant synergistic adsorption on the uniform growth of LiF-rich SEI, contributing to the reasonable design of electrolyte formulas and high-performance LMA, and enlightening the way for multi-scale simulation of SEI.

SI-ViT: Shuffle instance-based Vision Transformer for pancreatic cancer ROSE image classification.

BACKGROUND AND OBJECTIVE: The rapid on-site evaluation (ROSE) technique improves pancreatic cancer diagnosis by enabling immediate analysis of fast-stained cytopathological images. Automating ROSE classification could not only reduce the burden on pathologists but also broaden the application of this increasingly popular technique. However, this approach faces substantial challenges due to complex perturbations in color distribution, brightness, and contrast, which are influenced by various staining environments and devices. Additionally, the pronounced variability in cancerous patterns across samples further complicates classification, underscoring the difficulty in precisely identifying local cells and establishing their global relationships. METHODS: To address these challenges, we propose an instance-aware approach that enhances the Vision Transformer with a novel shuffle instance strategy (SI-ViT). Our approach presents a shuffle step to generate bags of shuffled instances and corresponding bag-level soft-labels, allowing the model to understand relationships and distributions beyond the limited original distributions. Simultaneously, combined with an un-shuffle step, the traditional ViT can model the relationships corresponding to the sample labels. This dual-step approach helps the model to focus on inner-sample and cross-sample instance relationships, making it potent in extracting diverse image patterns and reducing complicated perturbations. RESULTS: Compared to state-of-the-art methods, significant improvements in ROSE classification have been achieved. Aiming for interpretability, equipped with instance shuffling, SI-ViT yields precise attention regions that identifying cancer and normal cells in various scenarios. Additionally, the approach shows excellent potential in pathological image analysis through generalization validation on other datasets. CONCLUSIONS: By proposing instance relationship modeling through shuffling, we introduce a new insight in pathological image analysis. The significant improvements in ROSE classification leads to protential AI-on-site applications in pancreatic cancer diagnosis. The code and results are publicly available at https://github.com/sagizty/MIL-SI.

Design, synthesis and cytotoxicity of cell death mechanism of rotundic acid derivatives.

In the present investigation, 16 new rotundic acid (RA) derivatives modified at the C-3, C-23 and C-28 positions were synthesized. The cytotoxicities of the derivatives were evaluated against HeLa, A375, HepG2, SPC-A1 and NCI-H446 human tumor cell lines by MTT assay. Among these derivatives, compounds 4-7 exhibited stronger cell growth inhibitory than RA and compound 4 was found to be the best inhibition activity on five human tumor cell lines with IC50 <10 µM. The apoptosis mechanism of compound 4 in HeLa cells was investigated by western blot analysis. The results indicated that compound 4 could induce apoptosis through increasing protein expression of cleaved caspase-3 and Bax, and decreasing protein expression of Bcl-2. In summary, the present work suggests that compound 4 might serve as an effective chemotherapeutic candidate.

Three-Dimensional Bioprinting of Human iPSC-Derived Neuron-Astrocyte Cocultures for Screening Applications.

For a cell model to be viable for drug screening, the system must meet throughput and homogeneity requirements alongside having an efficient development time. However, many published 3D models do not satisfy these criteria. This therefore, limits their usefulness in early drug discovery applications. Three-dimensional (3D) bioprinting is a novel technology that can be applied to the development of 3D models to expedite development time, increase standardization, and increase throughput. Here, we present a protocol to develop 3D bioprinted coculture models of human induced pluripotent stem cell (iPSC)-derived glutamatergic neurons and astrocytes. These cocultures are embedded within a hydrogel matrix of bioactive peptides, full-length extracellular matrix (ECM) proteins, and with a physiological stiffness of 1.1 kPa. The model can be rapidly established in 96-well and 384-well formats and produces an average post-print viability of 72%. The astrocyte-to-neuron ratio in this model is shown to be 1:1.5, which is within the physiological range for the human brain. These 3D bioprinted cell populations also show expression of mature neural cell type markers and growth of neurite and astrocyte projections within 7 days of culture. As a result, this model is suitable for analysis using cell dyes and immunostaining techniques alongside neurite outgrowth assays. The ability to produce these physiologically representative models at scale makes them ideal for use in medium-to-high throughput screening assays for neuroscience targets.

PPG-based Continuous BP Waveform Estimation Using Polarized Attention-guided Conditional Adversarial Learning Model.

The blood pressure (BP) waveform is a vital source of physiological and pathological information concerning the cardiovascular system. This study proposes a novel attention-guided conditional generative adversarial network (cGAN), named PPG2BP-cGAN, to estimate BP waveforms based on photoplethysmography (PPG) signals. The proposed model comprises a generator and a discriminator. Specifically, the UNet3+-based generator integrates a full-scale skip connection structure with a modified polarized self-attention module based on a spatial-temporal attention mechanism. Additionally, its discriminator comprises PatchGAN, which augments the discriminative power of the generated BP waveform by increasing the perceptual field through fully convolutional layers. We demonstrate the superior BP waveform prediction performance of our proposed method compared to state-of-the-art (SOTA) techniques on two independent datasets. Our approach first pre-trained on a dataset containing 683 subjects and then tested on a public dataset. Experimental results from the Multi-parameter Intelligent Monitoring in Intensive Care dataset show that the proposed method achieves a root mean square error of 3.54, mean absolute error of 2.86, and Pearson coefficient of 0.99 for BP waveform estimation. Furthermore, the estimation errors (mean error ± standard deviation error) for systolic BP and diastolic BP are 0.72 ± 4.34 mmHg and 0.41 ± 2.48 mmHg, respectively, meeting the American Association for the Advancement of Medical Instrumentation standard. Our approach exhibits significant superiority over SOTA techniques on independent datasets, thus highlighting its potential for future applications in continuous cuffless BP waveform measurement.

D2-RecST: Dual-domain joint reconstruction strategy for fluorescence molecular tomography based on image domain and perception domain.

BACKGROUND AND OBJECTIVE: Fluorescence molecular tomography (FMT) is a promising molecular imaging modality for quantifying the three-dimensional (3D) distribution of fluorescent probes in small animals. Over the past few years, learning-based FMT reconstruction methods have achieved promising results. However, these methods typically attempt to minimize the mean-squared error (MSE) between the reconstructed image and the ground truth. Although signal-to-noise ratios (SNRs) are improved, they are susceptible to non-uniform artifacts and loss of structural detail, making it extremely challenging to obtain accurate and robust FMT reconstructions under noisy measurements. METHODS: We propose a novel dual-domain joint strategy based on the image domain and perception domain for accurate and robust FMT reconstruction. First, we formulate an explicit adversarial learning strategy in the image domain, which greatly facilitates training and optimization through two enhanced networks to improve anti-noise ability. Besides, we introduce a novel transfer learning strategy in the perceptual domain to optimize edge details by providing perceptual priors for fluorescent targets. Collectively, the proposed dual-domain joint reconstruction strategy can significantly eliminate the non-uniform artifacts and effectively preserve the structural edge details. RESULTS: Both numerical simulations and in vivo mouse experiments demonstrate that the proposed method markedly outperforms traditional and cutting-edge methods in terms of positioning accuracy, image contrast, robustness, and target morphological recovery. CONCLUSIONS: The proposed method achieves the best reconstruction performance and has great potential to facilitate precise localization and 3D visualization of tumors in in vivo animal experiments.

A Novel Feature Engineering Method Based on Latent Representation Learning for Radiomics: Application in NSCLC Subtype Classification.

Radiomics refers to the high-throughput extraction of quantitative features from medical images, and is widely used to construct machine learning models for the prediction of clinical outcomes, while feature engineering is the most important work in radiomics. However, current feature engineering methods fail to fully and effectively utilize the heterogeneity of features when dealing with different kinds of radiomics features. In this work, latent representation learning is first presented as a novel feature engineering approach to reconstruct a set of latent space features from original shape, intensity and texture features. This proposed method projects features into a subspace called latent space, in which the latent space features are obtained by minimizing a unique hybrid loss function including a clustering-like loss and a reconstruction loss. The former one ensures the separability among each class while the latter one narrows the gap between the original features and latent space features. Experiments were performed on a multi-center non-small cell lung cancer (NSCLC) subtype classification dataset from 8 international open databases. Results showed that compared with four traditional feature engineering methods (baseline, PCA, Lasso and L2,1-norm minimization), latent representation learning could significantly improve the classification performance of various machine learning classifiers on the independent test set (all p<0.001). Further on two additional test sets, latent representation learning also showed a significant improvement in generalization performance. Our research shows that latent representation learning is a more effective feature engineering method, which has the potential to be used as a general technology in a wide range of radiomics researches.

Robust reconstruction of fluorescence molecular tomography based on adaptive adversarial learning strategy.

Objective.Fluorescence molecular tomography (FMT) is a promising molecular imaging modality for quantifying the three-dimensional (3D) distribution of tumor probes in small animals. However, traditional deep learning reconstruction methods that aim to minimize the mean squared error (MSE) and iterative regularization algorithms that rely on optimal parameters are typically influenced by strong noise, resulting in poor FMT reconstruction robustness.Approach.In this letter, we propose an adaptive adversarial learning strategy (3D-UR-WGAN) to achieve robust FMT reconstructions. Unlike the pixel-based MSE criterion in traditional CNNs or the regularization strategy in iterative solving schemes, the reconstruction strategy can greatly facilitate the performance of the network models through alternating loop training of the generator and the discriminator. Second, the reconstruction strategy combines the adversarial loss in GANs with the L1 loss to significantly enhance the robustness and preserve image details and textual information.Main results.Both numerical simulations and physical phantom experiments demonstrate that the 3D-UR-WGAN method can adaptively eliminate the effects of different noise levels on the reconstruction results, resulting in robust reconstructed images with reduced artifacts and enhanced image contrast. Compared with the state-of-the-art methods, the proposed method achieves better reconstruction performance in terms of target shape recovery and localization accuracy.Significance.This adaptive adversarial learning reconstruction strategy can provide a possible paradigm for robust reconstruction in complex environments, and also has great potential to provide an alternative solution for solving the problem of poor robustness encountered in other optical imaging modalities such as diffuse optical tomography, bioluminescence imaging, and Cherenkov luminescence imaging.

FRESH™ 3D bioprinted cardiac tissue, a bioengineered platform for in vitro pharmacology.

There is critical need for a predictive model of human cardiac physiology in drug development to assess compound effects on human tissues. In vitro two-dimensional monolayer cultures of cardiomyocytes provide biochemical and cellular readouts, and in vivo animal models provide information on systemic cardiovascular response. However, there remains a significant gap in these models due to their incomplete recapitulation of adult human cardiovascular physiology. Recent efforts in developing in vitro models from engineered heart tissues have demonstrated potential for bridging this gap using human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) in three-dimensional tissue structure. Here, we advance this paradigm by implementing FRESH™ 3D bioprinting to build human cardiac tissues in a medium throughput, well-plate format with controlled tissue architecture, tailored cellular composition, and native-like physiological function, specifically in its drug response. We combined hiPSC-CMs, endothelial cells, and fibroblasts in a cellular bioink and FRESH™ 3D bioprinted this mixture in the format of a thin tissue strip stabilized on a tissue fixture. We show that cardiac tissues could be fabricated directly in a 24-well plate format were composed of dense and highly aligned hiPSC-CMs at >600 million cells/mL and, within 14 days, demonstrated reproducible calcium transients and a fast conduction velocity of ∼16 cm/s. Interrogation of these cardiac tissues with the ß-adrenergic receptor agonist isoproterenol showed responses consistent with positive chronotropy and inotropy. Treatment with calcium channel blocker verapamil demonstrated responses expected of hiPSC-CM derived cardiac tissues. These results confirm that FRESH™ 3D bioprinted cardiac tissues represent an in vitro platform that provides data on human physiological response.

Construction and validation of a novel lysosomal signature for hepatocellular carcinoma prognosis, diagnosis, and therapeutic decision-making.

Lysosomes is a well-recognized oncogenic driver and chemoresistance across variable cancer types, and has been associated with tumor invasiveness, metastasis, and poor prognosis. However, the significance of lysosomes in hepatocellular carcinoma (HCC) is not well understood. Lysosomes-related genes (LRGs) were downloaded from Genome Enrichment Analysis (GSEA) databases. Lysosome-related risk score (LRRS), including eight LRGs, was constructed via expression difference analysis (DEGs), univariate and LASSO-penalized Cox regression algorithm based on the TCGA cohort, while the ICGC cohort was obtained for signature validation. Based on GSE149614 Single-cell RNA sequencing data, model gene expression and liver tumor niche were further analyzed. Moreover, the functional enrichments, tumor microenvironment (TME), and genomic variation landscape between LRRSlow/LRRShigh subgroup were systematically investigated. A total of 15 Lysosomes-related differentially expressed genes (DELRGs) in HCC were detected, and then 10 prognosis DELRGs were screened out. Finally, the 8 optimal DELRGs (CLN3, GBA, CTSA, BSG, APLN, SORT1, ANXA2, and LAPTM4B) were selected to construct the LRRS prognosis signature of HCC. LRRS was considered as an independent prognostic factor and was associated with advanced clinicopathological features. LRRS also proved to be a potential marker for HCC diagnosis, especially for early-stage HCC. Then, a nomogram integrating the LRRS and clinical parameters was set up displaying great prognostic predictive performance. Moreover, patients with high LRRS showed higher tumor stemness, higher heterogeneity, and higher genomic alteration status than those in the low LRRS group and enriched in metabolism-related pathways, suggesting its underlying role in the progression and development of liver cancer. Meanwhile, the LRRS can affect the proportion of immunosuppressive cell infiltration, making it a vital immunosuppressive factor in the tumor microenvironment. Additionally, HCC patients with low LRRS were more sensitive to immunotherapy, while patients in the high LRRS group responded better to chemotherapy. Upon single-cell RNA sequencing, CLN3, GBA, and LAPTM4B were found to be specially expressed in hepatocytes, where they promoted cell progression. Finally, RT-qPCR and external datasets confirmed the mRNA expression levels of model genes. This study provided a direct links between LRRS signature and clinical characteristics, tumor microenvironment, and clinical drug-response, highlighting the critical role of lysosome in the development and treatment resistance of liver cancer, providing valuable insights into the prognosis prediction and treatment response of HCC, thereby providing valuable insights into prognostic prediction, early diagnosis, and therapeutic response of HCC.

Synthesis, characterization and cytotoxicity of new rotundic acid derivatives.

Rotundic acid (RA, 1), a natural compound, exhibits potent tumor cell growth inhibiting properties. To date there are no reports on derivatives of RA. Furthermore, the 28-COOH position of RA might make it unstable and induced serious gastrointestinal side effects when it was applied in vivo. Therefore, in order to explore and make use of this compound, eight new amino acid derivatives of RA at the 28-COOH position were synthesized and evaluated for their cytotoxicities in vitro on three tumor cell lines including A375, HepG2 and NCI-H446. As a result, a few of these new amino acid derivatives showed stronger cytotoxicity. Compound 5a was found to have the best inhibition activity on the three tested human tumor cell lines with IC(50) values of less than 10 µM compared with RA treatment. Meanwhile, the cytotoxicity of compound 6b was significantly higher than that of RA on the A375 cell line and almost the same as RA on the HepG2 and NCI-H446 cell lines. Hence, compounds 5a and 6b may serve as potential lead compounds for the development of new anti-tumor drugs.

Advances in Treatment Models of Advanced Gastric Cancer.

The prognosis of advanced gastric cancer (AGC) is extremely poor, and the therapeutic effect of traditional palliative chemotherapy is far from satisfactory. To overcome this bottleneck, palliative surgery resection, perioperative chemotherapy combined with surgical resection, hyperthermic intraperitoneal chemotherapy (HIPEC), pressurized intraperitoneal aerosol chemotherapy (PIPAC), radiation therapy, molecular-targeted therapy have been explored in AGC. Although considerable progress has been achieved, there is still no overwhelming therapeutic method. Due to the high heterogeneity of AGC, it is particularly vital to reshaped the paradigm of gastric cancer therapy according to the characteristics of clinical classifications and molecular subtypes.