Biomimetic nanocarriers loaded with temozolomide by cloaking brain-targeting peptides for targeting drug delivery system to promote anticancer effects in glioblastoma cells.

Glioma is the leading cancer of the central nervous system (CNS). The efficacy of glioma treatment is significantly hindered by the presence of the blood-brain barrier (BBB) and blood-brain tumour barrier (BBTB), which prevent most drugs from entering the brain and tumours. Hence, we established a novel drug delivery nanosystem of brain tumour-targeting that could self-assemble the method using an amphiphilic Zein protein isolated from corn. Zein's amphiphilicity prompted it to self-assembled into NPs, efficiently containing TMZ. This allowed us to investigate temozolomide (TMZ) for glioblastoma (GBM) treatment. To construct TMZ-encapsulated NPs (TMZ@RVG-Zein NPs), the NPs' Zein was clocked to rabies virus glycoprotein 29 (RVG29). To verify that the NPs could penetrate the BBB and precisely target and kill the GBM cancer cell line, in vitro studies were performed. The process of NPs penetrating cancer cell membranes was investigated using enzyme-linked immunosorbent assays (ELISAs) to measure the expressions of nicotinic acetylcholine receptors (nAChRs) on the U87 cell line. Therefore, effective targeted brain cancer treatment is possible by forming NP clocks, a cell-penetrating natural Zein protein with an RVG29. These NPs can penetrate the blood-brain barrier (BBB) and enter the glioblastoma (U87) cell line to release TMZ. These NPs have a distinct cocktail of biocompatibility and brain-targeting abilities, making them ideal for involving brain diseases.

Biomimetic fabrication of sr-silk fibroin co-assembly hydroxyapatite based microspheres with angiogenic and osteogenic properties for bone tissue engineering.

Bone defects caused by trauma, tumor resection, or developmental abnormalities are important issues in clinical practice. The vigorous development of tissue engineering technology provides new ideas and directions for regenerating bone defects. Hydroxyapatite (HAp), a bioactive ceramic, is extensively used in bone tissue engineering because of its excellent osteoinductive performance. However, its application is challenged by its single function and conventional environment-unfriendly synthesis methods. In this study, we successfully "green" synthesized sr-silk fibroin co-assembly hydroxyapatite nanoparticles (Sr-SF-HA) using silk fibroin (SF) as a biomineralized template, thus enabling it to have angiogenic activity and achieving the combination of organic and inorganic substances. Then, the rough composite microspheres loaded with Sr-SF-HA (CS/Sr-SF-HA) through electrostatic spraying technology and freeze-drying method were prepared. The CCK-8 test and live/dead cell staining showed excellent biocompatibility of CS/Sr-SF-HA. Alkaline phosphatase (ALP) staining, alizarin red staining (ARS), immunofluorescence, western blotting, and qRT-PCR test showed that CS/Sr-SF-HA activated the expression of related genes and proteins, thus inducing the osteogenic differentiation of rBMSCs. Moreover, tube formation experiments, scratch experiments, immunofluorescence, and qRT-PCR detection indicated that CS/Sr-SF-HA have good angiogenic activity. Furthermore, in vivo studies showed that the CS/Sr-SF-HA possesses excellent biocompatibility, vascular activity, as well as ectopic osteogenic ability in the subcutaneous pocket of rats. This study indicates that the construction of CS/Sr-SF-HA with angiogenic and osteogenic properties has great potential for bone tissue engineering.

Identification of RNA methylation-related lncRNAs for prognostic assessment and immunotherapy in bladder cancer-based on single cell/Bulk RNA sequencing data.

Bladder cancer is a malignancy characterized by significant heterogeneity. RNA methylation has received an increasing amount of attention in recent years. RNA data were collected from the GEO database, and cell subsets were classified according to specific cell markers. Epithelial, immunological, and fibroblast cells were clustered individually to explore the tumor heterogeneity. To distinguish between malignant and benign cells, the InferCNV R package was employed. The monocle2 R package was used for pseudotime analysis. The Decouple R package was used for transcription factor analysis of each cell subgroup, and PROGENy was used to predict the activity of pathways related to tumors. The target lncRNA was screened for model construction. In addition, the qPCR experiment was used to detect the transcription level of lncRNA. Epithelial cells, fibroblasts, and T cells significantly differ in tumor and normal tissues. The lncRNAs related to m6A/m5C/m1A were intersected to construct the model. Finally, six model lncRNAs (PSMB8-AS1, THUMPD3-AS1, U47924.27, XXbac-B135H6.15, MIR99AHG, and C14orf132) were screened. High-risk individuals were shown to have a better prognosis. qPCR experiments showed that the model lncRNA was differentially expressed between normal and tumor cells. Immunotherapy will be more effective in treating individuals with lower risk than those with higher risk using 4 candidate drugs. The prognostic m6A/m5C/m1A-related lncRNA model was constructed for evaluating the clinical outcomes of bladder cancer patients and guiding clinical medication.

Comprehending the cuproptosis and cancer-immunity cycle network: delving into the immune landscape and its predictive role in breast cancer immunotherapy responses and clinical endpoints.

Background: The role of cuproptosis, a phenomenon associated with tumor metabolism and immunological identification, remains underexplored, particularly in relation to the cancer-immunity cycle (CIC) network. This study aims to rigorously examine the impact of the cuproptosis-CIC nexus on immune reactions and prognostic outcomes in patients with breast cancer (BC), striving to establish a comprehensive prognostic model. Methods: In the study, we segregated data obtained from TCGA, GEO, and ICGC using CICs retrieved from the TIP database. We constructed a genetic prognostic framework using the LASSO-Cox model, followed by its validation through Cox proportional hazards regression. This framework's validity was further confirmed with data from ICGC and GEO. Explorations of the tumor microenvironment were carried out through the application of ESTIMATE and CIBERSORT algorithms, as well as machine learning techniques, to identify potential treatment strategies. Single-cell sequencing methods were utilized to delineate the spatial distribution of key genes within the various cell types in the tumor milieu. To explore the critical role of the identified CICs, experiments were conducted focusing on cell survival and migration abilities. Results: In our research, we identified a set of 4 crucial cuproptosis-CICs that have a profound impact on patient longevity and their response to immunotherapy. By leveraging these identified CICs, we constructed a predictive model that efficiently estimates patient prognoses. Detailed analyses at the single-cell level showed that the significance of CICs. Experimental approaches, including CCK-8, Transwell, and wound healing assays, revealed that the protein HSPA9 restricts the growth and movement of breast cancer cells. Furthermore, our studies using immunofluorescence techniques demonstrated that suppressing HSPA9 leads to a notable increase in ceramide levels. Conclusion: This research outlines a network of cuproptosis-CICs and constructs a predictive nomogram. Our model holds great promise for healthcare professionals to personalize treatment approaches for individuals with breast cancer. The work provides insights into the complex relationship between the cuproptosis-CIC network and the cancer immune microenvironment, setting the stage for novel approaches to cancer immunotherapy. By focusing on the essential gene HSPA9 within the cancer-immunity cycle, this strategy has the potential to significantly improve the efficacy of treatments against breast cancer.

Causal relationship between gut microbiota and risk of esophageal cancer: evidence from Mendelian randomization study.

BACKGROUND: The causative implications remain ambiguous. Consequently, this study aims to evaluate the putative causal relationship between gut microbiota and Esophageal cancer (EC). METHODS: The genome-wide association study (GWAS) pertaining to the microbiome, derived from the MiBioGen consortium-which consolidates 18,340 samples across 24 population-based cohorts-was utilized as the exposure dataset. Employing the GWAS summary statistics specific to EC patients sourced from the GWAS Catalog and leveraging the two-sample Mendelian randomization (MR) methodology, the principal analytical method applied was the inverse variance weighted (IVW) technique. Cochran's Q statistic was utilized to discern heterogeneity inherent in the data set. Subsequently, a reverse MR analysis was executed. RESULTS: Findings derived from the IVW technique elucidated that the Family Porphyromonadaceae (P = 0.048) and Genus Candidatus Soleaferrea (P = 0.048) function as deterrents against EC development. In contrast, the Genus Catenibacterium (P = 0.044), Genus Eubacterium coprostanoligenes group (P = 0.038), Genus Marvinbryantia (P = 0.049), Genus Ruminococcaceae UCG010 (P = 0.034), Genus Ruminococcus1 (P = 0.047), and Genus Sutterella (P = 0.012) emerged as prospective risk contributors for EC. To assess reverse causal effect, we used EC as the exposure and the gut microbiota as the outcome, and this analysis revealed associations between EC and seven different types of gut microbiota. The robustness of the MR findings was substantiated through comprehensive heterogeneity and pleiotropy evaluations. CONCLUSIONS: This research identified certain microbial taxa as either protective or detrimental elements for EC, potentially offering valuable biomarkers for asymptomatic diagnosis and prospective therapeutic interventions for EC.

Glycogen metabolism-mediated intercellular communication in the tumor microenvironment influences liver cancer prognosis.

Glycogen metabolism plays a key role in the development of hepatocellular carcinoma (HCC), but the function of glycogen metabolism genes in the tumor microenvironment (TME) is still to be elucidated. Single-cell RNA-seq data were obtained from ten HCC tumor samples totaling 64,545 cells, and 65 glycogen metabolism genes were analyzed by a nonnegative matrix factorization (NMF). The prognosis and immune response of new glycogen TME cell clusters were predicted by using HCC and immunotherapy cohorts from public databases. HCC single-cell analysis was divided into fibroblasts, NT T cells, macrophages, endothelial cells, and B cells, which were separately divided into new cell clusters by glycogen metabolism gene annotation. Pseudo-temporal trajectory analysis demonstrated the temporal differentiation trajectory of different glycogen subtype cell clusters. Cellular communication analysis revealed extensive interactions between endothelial cells with glycogen metabolizing TME cell-related subtypes and different glycogen subtype cell clusters. SCENIC analysis of transcription factors upstream of TME cell clusters with different glycogen metabolism. In addition, TME cell clusters of glycogen metabolism were found to be enriched in expression in CAF subtypes, CD8 depleted, M1, and M2 types. Bulk-seq analysis showed the prognostic significance of glycogen metabolism-mediated TME cell clusters in HCC, while a significant immune response was found in the immunotherapy cohort in patients treated with immune checkpoint blockade (ICB), especially for CAFs, T cells, and macrophages. In summary, our study reveals for the first time that glycogen metabolism mediates intercellular communication in the hepatocellular carcinoma microenvironment while elucidating the anti-tumor mechanisms and immune prognostic responses of different subtypes of cell clusters.

Unveiling Anoikis-related genes: A breakthrough in the prognosis of bladder cancer.

BACKGROUND: Bladder cancer (BLCA) is a prevalent malignancy worldwide. Anoikis remains a new form of cell death. It is necessary to explore Anoikis-related genes in the prognosis of BLCA. METHODS: We obtained RNA expression profiles from the The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases for dimensionality reduction analysis and isolated epithelial cells, T cells and fibroblasts for copy number variation analysis, pseudotime analysis and transcription factor analysis based on R package. We integrated machine-learning algorithms to develop the artificial intelligence-derived prognostic signature (AIDPS). RESULTS: The performance of AIDPS with clinical indicators was stable and robust in predicting BLCA and showed better performance in every validation dataset compared to other models. Mendelian randomization analysis was conducted. Single nucleotide polymorphism (SNP) sites of rs3100578 (HK2) and rs66467677 (HSP90B1) exhibited significant correlation of bladder problem (not cancer) and bladder cancer, whereasSNP sites of rs3100578 (HK2) and rs947939 (BAD) had correlation between bladder stone and bladder cancer. The immune infiltration analysis of the TCGA-BLCA cohort was calculated via the ESTIMATE (i.e. Estimation of STromal and Immune cells in MAlignantTumours using Expression data) algorithm which contains stromal, immune and estimate scores. We also found significant differences in the IC50 values of Bortezomib_1191, Docetaxel_1007, Staurosporine_1034 and Rapamycin_1084 among the high- and low-risk groups. CONCLUSIONS: In conclusion, these findings indicated Anoikis-related prognostic genes in BLCA and constructed an innovative machine-learning model of AIDPS with high prognostic value for BLCA.

Establishment and Validation of Novel Prognostic Subtypes in Hepatocellular Carcinoma Based on Bile Acid Metabolism Gene Signatures Using Bulk and Single-Cell RNA-Seq Data.

Hepatocellular carcinoma (HCC) is a highly detrimental cancer type and has limited therapeutic options, posing significant threats to human health. The development of HCC has been associated with a disorder in bile acid (BA) metabolism. In this study, we employed an integrative approach, combining various datasets and omics analyses, to comprehensively characterize the tumor microenvironment in HCC based on genes related to BA metabolism. Our analysis resulted in the classification of HCC samples into four subtypes (C1, C2a, C2b, and C3). Notably, subtype C2a, characterized by the highest bile acid metabolism score (BAMS), exhibited the highest survival probability. This subtype also demonstrated increased immune cell infiltration, lower cell cycle scores, reduced AFP levels, and a lower risk of metastasis compared to subtypes C1 and C3. Subtype C1 displayed poorer survival probability and elevated cell cycle scores. Importantly, the identified subtypes based on BAMS showed potential relevance to the gene expression of drug targets in currently approved drugs and those under clinical research. Genes encoding VEGFR (FLT4 and KDR) and MET were elevated in C2, while genes such as TGFBR1, TGFB1, ADORA3, SRC, BRAF, RET, FLT3, KIT, PDGFRA, and PDGFRB were elevated in C1. Additionally, FGFR2 and FGFR3, along with immune target genes including PDCD1 and CTLA4, were higher in C3. This suggests that subtypes C1, C2, and C3 might represent distinct potential candidates for TGFB1 inhibitors, VEGFR inhibitors, and immune checkpoint blockade treatments, respectively. Significantly, both bulk and single-cell transcriptome analyses unveiled a negative correlation between BA metabolism and cell cycle-related pathways. In vitro experiments further confirmed that the treatment of HCC cell lines with BA receptor agonist ursodeoxycholic acid led to the downregulation of the expression of cell cycle-related genes. Our findings suggest a plausible involvement of BA metabolism in liver carcinogenesis, potentially mediated through the regulation of tumor cell cycles and the immune microenvironment. This preliminary understanding lays the groundwork for future investigations to validate and elucidate the specific mechanisms underlying this potential association. Furthermore, this study provides a novel foundation for future precise molecular typing and the design of systemic clinical trials for HCC therapy.

Hypoxia-based critical gene biomarkers as prognostic reporters for gastric adenocarcinoma.

BACKGROUND: Gastric cancer is the most common malignant tumour of the digestive system, yet there is a lack of reported prognostic biomarkers for STAD patients. METHODS: Transcriptomic expression data of STAD from GEO database, single cell sequencing data from OMIX gastric cancer database. Conservative molecular typing of gastric cancer was constructed using non-negative matrix factorization (NMF). The abundance of 28 immune cells in the tumour samples was assessed using ssGSEA. The R package "oncoPredict" was used to predict chemotherapy response. TIDE website for immunotherapy response prediction. Finally, single cell analysis was performed to clarify the specific type annotation of STAD cells and to analysis their spatial expression. RESULTS: Hypoxia-score demonstrated excellent prognostic discrimination in TCGA gastric cancer samples. Among multiple deconvolution-based algorithms for immune infiltration, Hypoxia-score presented a general immunosuppressive efficacy across multiple datasets, as evidenced by a broad negative correlation with immune cell infiltration. By the likelihood that each group may have specific drug sensitivity to multiple chemotherapeutic and targeted agents. Results showed that high-risk scoring patients were more sensitive to Staurosporine, Sabutoclax, and AZD8055, while low-risk patients were more sensitive to Bortezomib, Dactinomycin, Docetaxel, Daporinad, Sepantronium, and bromide. In the immunotherapy cohort, the Hypoxia-score presented the ability to discriminate for immunotherapy efficacy. The distribution of Hypoxia-score in single-cell descending space was calculated using AddModuleScore and was found to be distributed across the various cell types annotated in the single-cell analysis. It is suggested that various cells in the tumour microenvironment are involved in hypoxia gene set processes to varying degrees. CONCLUSION: The Hypoxia-score proves to be a valuable tool for assessing the prognosis of gastric cancer patients and guiding drug treatments, providing significant guidance for clinical diagnosis and treatment in the context of gastric cancer.

Coordinating single-cell and bulk RNA-seq in deciphering the intratumoral immune landscape and prognostic stratification of prostate cancer patients.

INTRODUCTION: Prostate cancer is a common cancer among male population. The aberrant expression of histone modifiers has been identified as a potential driving force in numerous cancer types. However, the mechanism of histone modifiers in the development of prostate cancer remains unknown. METHODS: Expression profiles and clinical data were obtained from GSE70769, GSE46602, and GSE67980. Seruat R package was utilized to calculate the gene set enrichment of the histone modification pathway and obtain the Histone score. Least absolute shrinkage and selection operator (LASSO) and Cox regression analyses were employed to identify marker genes with prognostic value. Kaplan-Meier survival analysis was conducted to assess the efficacy of the prognostic model. In addition, microenvironment cell populations counter (MCPcounter), single-sample gene set enrichment analysis (ssGSEA), and xCell algorithms were employed for immune infiltration analysis. Drug sensitivity prediction was performed using oncoPredict R package. RESULTS: We screened differentially expressed genes (DEGs) between Histone-high score (Histone-H) and Histone-low score (Histone-L) groups, which were enriched in RNA splicing and DNA-binding transcription factor binding pathways. We retained four prognostic marker genes, including TACC3, YWHAH, TAF1C and TTLL5. The risk model showed significant efficacy in stratification of the prognosis of prostate cancer patients in both internal and external cohorts (p < .0001 and p = .032, respectively). In addition, prognostic gene YWHAH was infiltrated in abundance of fibroblasts and highly correlated with Entinostat_1593 drug sensitivity score and the value of risk score. CONCLUSION: We innovatively developed a histone modification-related prognostic model with high prognostic potency and identified YWHAH as possible diagnostic and therapeutic biomarkers for prostate cancer. It provides novel insights to address prostate cancer and enhance clinical outcomes, thereby opening up a new avenue for customized treatment alternatives.

Investigating the prognostic role of lncRNAs associated with disulfidptosis-related genes in clear cell renal cell carcinoma.

INTRODUCTION: Renal cell carcinoma (RCC) is a grave malignancy that poses a significant global health burden with over 400,000 new cases annually. Disulfidptosis, a newly discovered programmed cell death process, is linked to the actin cytoskeleton, which plays a vital role in maintaining cell shape and survival. The role of disulfidptosis is poorly depicted in the clear cell histologic variant of RCC (ccRCC). METHODS: Three sets of ccRCC cohorts, ICGC_RECA-EU (n = 91), GSE76207 (n = 32) and TCGA-KIRC (n = 607), were included in our study, the batch effect of which was removed using the "combat" function. Correlation was calculated using the "rcorr" function of the "Hmisc" package for Pearson analysis, which was visualized using the "pheatmap" package. Principal component analysis was performed by the "vegan" package, visualized using the "scatterplot3d" package. Long non-coding RNAs (lncRNAs) associated with disulfidptosis were screened out using least absolute shrinkage and selection operator (LASSO) and COX analysis. Tumor mutation, immune landscaping and immunotherapy prediction were performed for further characterization of two risk groups. RESULTS: A total of 1822 disulfidptosis-related lncRNAs was selected, among which 308 lncRNAs were found to be significantly associated with the clinical outcome of ccRCC patients. We retained 11 disulfidptosis-related lncRNAs, namely, AP000439.3, RP11-417E7.1, RP11-119D9.1, LINC01510, SNHG3, AC156455.1, RP11-291B21.2, EMX2OS, AC093850.2, HAGLR and RP11-389C8.2, through LASSO and COX analysis for prognosis model construction, which displayed satisfactory accuracy (area under the curve, AUC, values all above 0.6 in multiple cohorts) in stratification of ccRCC prognosis. A nomogram model was constructed by integrating clinical factors with risk score, which further enhanced the prediction efficacy (AUC values all above 0.7 in multiple cohorts). We found that patients of male gender, higher clinical stages and advanced pathological T stage were inclined to have higher risk score values. Dactinomycin_1911, Vinblastine_1004, Daporinad_1248 and Vinorelbine_2048 were identified as promising candidate drugs for treating ccRCC patients of higher risk score value. Moreover, patients of higher risk value were prone to be resistant to immunotherapy. CONCLUSION: We developed a prognosis predicting model based on 11 selected disulfidptosis-related lncRNAs, the efficacy of which was verified in different cohorts. Furthermore, we delineated an intricate portrait of tumor mutation, immune topography and pharmacosensitivity evaluations within disparate risk stratifications.

Tigecycline causes loss of cell viability mediated by mitochondrial OXPHOS and RAC1 in hepatocellular carcinoma cells.

BACKGROUND: Despite recent advances in locoregional, systemic, and novel checkpoint inhibitor treatment, hepatocellular carcinoma (HCC) is still associated with poor prognosis. The feasibility of potentially curative liver resection (LR) and transplantation (LT) is limited by the underlying liver disease and a shortage of organ donors. Especially after LR, high recurrence rates present a problem and circulating tumor cells are a major cause of extrahepatic recurrence. Tigecycline, a commonly used glycylcycline antibiotic, has been shown to have antitumorigenic effects and could be used as a perioperative and adjuvant therapeutic strategy to target circulating tumor cells. We aimed to investigate the effect of tigecycline on HCC cell lines and its mechanisms of action. METHODS: Huh7, HepG2, Hep3B, and immortalized hepatocytes underwent incubation with clinically relevant tigecycline concentrations, and the influence on proliferation, migration, and invasion was assessed in two- and three-dimensional in vitro assays, respectively. Bioinformatic analysis was used to identify specific targets of tigecycline. The expression of RAC1 was detected using western blot, RT-PCR and RNA sequencing. ELISA and flow cytometry were utilized to measure reactive oxygen species (ROS) generation upon tigecycline treatment and flow cytometry to detect alterations in cell cycle. Changes in mitochondrial function were detected via seahorse analysis. RNA sequencing was performed to examine involved pathways. RESULTS: Tigecycline treatment resulted in a significant reduction of mitochondrial function with concomitantly preserved mitochondrial size, which preceded the observed decrease in HCC cell viability. The sensitivity of HCC cells to tigecycline treatment was higher than that of immortalized non-cancerous THLE-2 hepatocytes. Tigecycline inhibited both migratory and invasive properties. Tigecycline application led to an increase of detected ROS and an S-phase cell cycle arrest. Bioinformatic analysis identified RAC1 as a likely target for tigecycline and the expression of this molecule was increased in HCC cells as a result of tigecycline treatment. CONCLUSION: Our study provides evidence for the antiproliferative effect of tigecycline in HCC. We show for the first time that this effect, likely to be mediated by reduced mitochondrial function, is associated with increased expression of RAC1. The reported effects of tigecycline with clinically relevant and achievable doses on HCC cells lay the groundwork for a conceivable use of this agent in cancer treatment.

Discovery and Characterization of MaK: A Novel Knottin Antimicrobial Peptide from Monochamus alternatus.

Knottin-type antimicrobial peptides possess exceptional attributes, such as high efficacy, low vulnerability to drug resistance, minimal toxicity, and precise targeting of drug sites. These peptides play a crucial role in the innate immunity of insects, offering protection against bacteria, fungi, and parasites. Knottins have garnered considerable interest as promising contenders for drug development due to their ability to bridge the gap between small molecules and protein-based biopharmaceuticals, effectively addressing the therapeutic limitations of both modalities. This work presents the isolation and identification of a novel antimicrobial peptide derived from Monochamus alternatus. The cDNA encodes a 56-amino acid knottin propeptide, while the mature peptide comprises only 34 amino acids. We have labeled this knottin peptide as MaK. Using chemically synthesized MaK, we evaluated its hemolytic activity, thermal stability, antibacterial properties, and efficacy against nematodes. The results of this study indicate that MaK is an exceptionally effective knottin-type peptide. It demonstrates low toxicity, superior stability, potent antibacterial activity, and the ability to suppress pine wood nematodes. Consequently, these findings suggest that MaK has potential use in developing innovative therapeutic agents to prevent and manage pine wilt disease.

Cucurbitacin I Reverses Tumor-Associated Macrophage Polarization to Affect Cancer Cell Metastasis.

The tumor microenvironment plays a critical role in tumor progression and immune regulation. As one of the most important components of the tumor microenvironment, macrophages have become a new therapeutic target for inhibiting tumor progression. Despite the well-documented anticancer activity of cucurbitacin I, its effect on macrophages remains unclear. In this study, we established a coculture system of macrophages and cancer cells under hypoxic conditions to simulate the tumor-promoting environment mediated by M2-like macrophages. We determined whether cucurbitacin I modulates M2-like polarization in macrophages in vitro and conducted RNA sequencing to identify gene expression changes induced by cucurbitacin I in macrophages. The results indicated a remarkable inhibition of the M2-like polarization phenotype in macrophages following treatment with cucurbitacin I, which was accompanied by the significant downregulation of heme oxygenase-1. Moreover, we found that cucurbitacin I-treated macrophages reduced the migration of cancer cells by inhibiting the M2 polarization in vitro. These findings highlight the potential of cucurbitacin I as a therapeutic agent that targets M2-like macrophages to inhibit cancer cell metastasis. Our study provides novel insights into the intricate interplay among macrophage polarization, cucurbitacin I, and heme oxygenase-1, thereby opening new avenues for cancer treatment.

Combined Inhibition of UBE2C and PLK1 Reduce Cell Proliferation and Arrest Cell Cycle by Affecting ACLY in Pan-Cancer.

Various studies have shown that the cell-cycle-related regulatory proteins UBE2C, PLK1, and BIRC5 promote cell proliferation and migration in different types of cancer. However, there is a lack of in-depth and systematic research on the mechanism of these three as therapeutic targets. In this study, we found a positive correlation between the expression of UBE2C and PLK1/BIRC5 in the Cancer Genome Atlas (TCGA) database, revealing a potential combination therapy candidate for pan-cancer. Quantitative real-time PCR (qRT-PCR), Western blotting (WB), cell phenotype detection, and RNA-seq techniques were used to evidence the effectiveness of the combination candidate. We found that combined interference of UBE2C with PLK1 and UBE2C with BIRC5 affected metabolic pathways by significantly downregulating the mRNA expression of IDH1 and ACLY, which was related to the synthesis of acetyl-CoA. By combining the PLK1 inhibitor volasertib and the ACLY inhibitor bempedoic acid, it showed a higher synergistic inhibition of cell viability and higher synergy scores in seven cell lines, compared with those of other combination treatments. Our study reveals the potential mechanisms through which cell-cycle-related genes regulate metabolism and proposes a potential combined targeted therapy for patients with higher PLK1 and ACLY expression in pan-cancer.

Exploring oncogenes for renal clear cell carcinoma based on G protein-coupled receptor-associated genes.

G protein-coupled receptors (GPCRs) are a class of receptors on cell membranes that regulate various biological processes in cells, such as cell proliferation, differentiation, migration, apoptosis, and metabolism, by interacting with G proteins. However, the role of G protein-coupled receptors in predicting the prognosis of renal clear cell carcinoma is still unknown. The transcriptome data and clinical profiles of renal clear cell carcinoma patients, were downloaded from TCGA databases, and the validation group data were downloaded from number GSE167573, including 63 tumor samples and 14 normal samples. Single-cell RNA sequencing data were downloaded from the GEO database, No. GSE152938 and selected samples were used for GSEA enrichment analysis, WGCNA subgroup analysis, single-cell data analysis, and mutation analysis to explore the role of G protein-coupled receptor-related genes in the diagnosis and prognosis of renal clear cell carcinoma and to verify their reliability with cellular experiments. Finally, this study establishes a disease model based on G protein-coupled receptor-related genes, which may help to propose targeted therapeutic regimens in different strata of renal cell carcinoma patients.Author names: Please confirm if the author names are presented accurately and in the correct sequence (given name, middle name/initial, family name). Author: Given name [Lisa Jia] Last name [Tran].It's ok!

Integrated multi-omics identified the novel intratumor microbiome-derived subtypes and signature to predict the outcome, tumor microenvironment heterogeneity, and immunotherapy response for pancreatic cancer patients.

Background: The extremely malignant tumour known as pancreatic cancer (PC) lacks efficient prognostic markers and treatment strategies. The microbiome is crucial to how cancer develops and responds to treatment. Our study was conducted in order to better understand how PC patients' microbiomes influence their outcome, tumour microenvironment, and responsiveness to immunotherapy. Methods: We integrated transcriptome and microbiome data of PC and used univariable Cox regression and Kaplan-Meier method for screening the prognostic microbes. Then intratumor microbiome-derived subtypes were identified using consensus clustering. We utilized LASSO and Cox regression to build the microbe-related model for predicting the prognosis of PC, and utilized eight algorithms to assess the immune microenvironment feature. The OncoPredict package was utilized to predict drug treatment response. We utilized qRT-PCR to verify gene expression and single-cell analysis to reveal the composition of PC tumour microenvironment. Results: We obtained a total of 26 prognostic genera in PC. And PC samples were divided into two microbiome-related subtypes: Mcluster A and B. Compared with Mcluster A, patients in Mcluster B had a worse prognosis and higher TNM stage and pathological grade. Immune analysis revealed that neutrophils, regulatory T cell, CD8+ T cell, macrophages M1 and M2, cancer associated fibroblasts, myeloid dendritic cell, and activated mast cell had remarkably higher infiltrated levels within the tumour microenvironment of Mcluster B. Patients in Mcluster A were more likely to benefit from CTLA-4 blockers and were highly sensitive to 5-fluorouracil, cisplatin, gemcitabine, irinotecan, oxaliplatin, and epirubicin. Moreover, we built a microbe-derived model to assess the outcome. The ROC curves showed that the microbe-related model has good predictive performance. The expression of LAMA3 and LIPH was markedly increased within pancreatic tumour tissues and was linked to advanced stage and poor prognosis. Single-cell analysis indicated that besides cancer cells, the tumour microenvironment of PC was also rich in monocytes/macrophages, endothelial cells, and fibroblasts. LIPH and LAMA3 exhibited relatively higher expression in cancer cells and neutrophils. Conclusion: The intratumor microbiome-derived subtypes and signature in PC were first established, and our study provided novel perspectives on PC prognostic indicators and treatment options.

GADD45B regulates the carcinogenesis process of chronic atrophic gastritis and the metabolic pathways of gastric cancer.

Background: Gastric cancer continues to be a significant global healthcare challenge, and its burden remains substantial. The development of gastric cancer (GC) is closely linked to chronic atrophic gastritis (CAG), yet there is a scarcity of research exploring the underlying mechanisms of CAG-induced carcinogenesis. Methods: In this study, we conducted a comprehensive investigation into the oncogenes involved in CAG using both bulk transcriptome and single-cell transcriptome data. Our approach employed hdWGCNA to identify pathogenic genes specific to CAG, with non-atrophic gastritis (NAG) serving as the control group. Additionally, we compared CAG with GC, using normal gastric tissue as the control group in the single-cell transcriptome analysis. By intersecting the identified pathogenic genes, we pinpointed key network molecules through protein interaction network analysis. To further refine the gene selection, we applied LASSO, SVM-RFE, and RF techniques, which resulted in a set of cancer-related genes (CRGs) associated with CAG. To identify CRGs potentially linked to gastric cancer progression, we performed a univariate COX regression analysis on the gene set. Subsequently, we explored the relationship between CRGs and immune infiltration, drug sensitivity, and clinical characteristics in gastric cancer patients. We employed GSVA to investigate how CRGs regulated signaling pathways in gastric cancer cells, while an analysis of cell communication shed light on the impact of CRGs on signal transmission within the gastric cancer tumor microenvironment. Lastly, we analyzed changes in metabolic pathways throughout the progression of gastric cancer. Results: Using hdWGCNA, we have identified a total of 143 pathogenic genes that were shared by CAG and GC. To further investigate the underlying mechanisms, we conducted protein interaction network analysis and employed machine learning screening techniques. As a result, we have identified 15 oncogenes that are specifically associated with chronic atrophic gastritis. By performing ROC reanalysis and prognostic analysis, we have determined that GADD45B is the most significant gene involved in the carcinogenesis of CAG. Immunohistochemical staining and differential analysis have revealed that GADD45B expression was low in GC tissues while high in normal gastric tissues. Moreover, based on prognostic analysis, high expression of GADD45B has been correlated with poor prognosis in GC patients. Additionally, an analysis of immune infiltration has shown a relationship between GADD45B and the infiltration of various immune cells. By correlating GADD45B with clinical characteristics, we have found that it primarily affects the depth of invasion in GC. Through cell communication analysis, we have discovered that the CD99 signaling pathway network and the CDH signaling pathway network are the main communication pathways that significantly alter the microenvironment of gastric tissue during the development of chronic atrophic gastritis. Specifically, GADD45B-low GC cells were predominantly involved in the network communication of the CDH signaling pathway, while GADD45B-high GC cells played a crucial role in both signaling pathways. Furthermore, we have identified several metabolic pathways, including D-Glutamine and D-glutamate metabolism and N-Glycan biosynthesis, among others, that played important roles in the occurrence and progression of GC, in addition to the six other metabolic pathways. In summary, our study highlighted the discovery of 143 pathogenic genes shared by CAG and GC, with a specific focus on 15 oncogenes associated with CAG. We have identified GADD45B as the most important gene in the carcinogenesis of CAG, which exhibited differential expression in GC tissues compared to normal gastric tissues. Moreover, GADD45B expression was correlated with patient prognosis and is associated with immune cell infiltration. Our findings also emphasized the impact of the CD99 and CDH signaling pathway networks on the microenvironment of gastric tissue during the development of CAG. Additionally, we have identified key metabolic pathways involved in GC progression. Conclusion: GADD45B, an oncogene implicated in chronic atrophic gastritis, played a critical role in GC development. Decreased expression of GADD45B was associated with the onset of GC. Moreover, GADD45B expression levels were closely tied to poor prognosis in GC patients, influencing the infiltration patterns of various cells within the tumor microenvironment, as well as impacting the metabolic pathways involved in GC progression.

Proposing new early detection indicators for pancreatic cancer: Combining machine learning and neural networks for serum miRNA-based diagnostic model.

Background: Pancreatic cancer (PC) is a lethal malignancy that ranks seventh in terms of global cancer-related mortality. Despite advancements in treatment, the five-year survival rate remains low, emphasizing the urgent need for reliable early detection methods. MicroRNAs (miRNAs), a group of non-coding RNAs involved in critical gene regulatory mechanisms, have garnered significant attention as potential diagnostic and prognostic biomarkers for pancreatic cancer (PC). Their suitability stems from their accessibility and stability in blood, making them particularly appealing for clinical applications. Methods: In this study, we analyzed serum miRNA expression profiles from three independent PC datasets obtained from the Gene Expression Omnibus (GEO) database. To identify serum miRNAs associated with PC incidence, we employed three machine learning algorithms: Support Vector Machine-Recursive Feature Elimination (SVM-RFE), Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest. We developed an artificial neural network model to assess the accuracy of the identified PC-related serum miRNAs (PCRSMs) and create a nomogram. These findings were further validated through qPCR experiments. Additionally, patient samples with PC were classified using the consensus clustering method. Results: Our analysis revealed three PCRSMs, namely hsa-miR-4648, hsa-miR-125b-1-3p, and hsa-miR-3201, using the three machine learning algorithms. The artificial neural network model demonstrated high accuracy in distinguishing between normal and pancreatic cancer samples, with verification and training groups exhibiting AUC values of 0.935 and 0.926, respectively. We also utilized the consensus clustering method to classify PC samples into two optimal subtypes. Furthermore, our investigation into the expression of PCRSMs unveiled a significant negative correlation between the expression of hsa-miR-125b-1-3p and age. Conclusion: Our study introduces a novel artificial neural network model for early diagnosis of pancreatic cancer, carrying significant clinical implications. Furthermore, our findings provide valuable insights into the pathogenesis of pancreatic cancer and offer potential avenues for drug screening, personalized treatment, and immunotherapy against this lethal disease.