Inhibition of aquaporin-9 ameliorates severe acute pancreatitis and associated lung injury by NLRP3 and Nrf2/HO-1 pathways.

Inflammation, apoptosis and oxidative stress play crucial roles in the deterioration of severe acute pancreatitis-associated acute respiratory distress syndrome (SAP-ARDS). Unfortunately, despite a high mortality rate of 45 %[1], there are limited treatment options available for ARDS outside of last resort options such as mechanical ventilation and extracorporeal support strategies[2]. This study investigated the potential therapeutic role and mechanisms of AQP9 inhibitor RG100204 in two animal models of severe acute pancreatitis, inducing acute respiratory distress syndrome: 1) a sodium-taurocholate induced rat model, and 2) and Cerulein and lipopolysaccharide induced mouse model. RG100204 treatment led to a profound reduction in inflammatory cytokine expression in pancreatic, and lung tissue, in both models. In addition, infiltration of CD68 + and CD11b + cells into these tissues were reduced in RG100204 treated SAP animals, and edema and SAP associated tissue damage were improved. Moreover, we demonstrate that RG100204 reduced apoptosis in the lungs of rat SAP animals, and reduces NF-κB signaling, NLRP3, expression, while profoundly increasing the Nrf2-dependent anti oxidative stress response. We conclude that AQP9 inhibition is a promising strategy for the treatment of pancreatitis and its systemic complications, such as ARDS.

Deep pan-cancer analysis and multi-omics evidence reveal that ALG3 inhibits CD8+ T cell infiltration by suppressing chemokine secretion and is associated with 5-fluorouracil sensitivity.

BACKGROUND: α-1,3-mannosyltransferase (ALG3) holds significance as a key member within the mannosyltransferase family. Nevertheless, the exact function of ALG3 in cancer remains ambiguous. Consequently, the current research aimed to examine the function and potential mechanisms of ALG3 in various types of cancer. METHODS: Deep pan-cancer analyses were conducted to investigate the expression patterns, prognostic value, genetic variations, single-cell omics, immunology, and drug responses associated with ALG3. Subsequently, in vitro experiments were executed to ascertain the biological role of ALG3 in breast cancer. Moreover, the link between ALG3 and CD8+ T cells was verified using immunofluorescence. Lastly, the association between ALG3 and chemokines was assessed using qRT-PCR and ELISA. RESULTS: Deep pan-cancer analysis demonstrated a heightened expression of ALG3 in the majority of tumors based on multi-omics evidence. ALG3 emerges as a diagnostic and prognostic biomarker across diverse cancer types. In addition, ALG3 participates in regulating the tumor immune microenvironment. Elevated levels of ALG3 were closely linked to the infiltration of bone marrow-derived suppressor cells (MDSC) and CD8+ T cells. According to in vitro experiments, ALG3 promotes proliferation and migration of breast cancer cells. Moreover, ALG3 inhibited CD8+ T cell infiltration by suppressing chemokine secretion. Finally, the inhibition of ALG3 enhanced the responsiveness of breast cancer cells to 5-fluorouracil treatment. CONCLUSION: ALG3 shows potential as both a prognostic indicator and immune infiltration biomarker across various types of cancer. Inhibition of ALG3 may represent a promising therapeutic strategy for tumor treatment.

A pan-cancer multi-omics analysis of lactylation genes associated with tumor microenvironment and cancer development.

Background: Lactylation is a significant post-translational modification bridging the gap between cancer epigenetics and metabolic reprogramming. However, the association between lactylation and prognosis, tumor microenvironment (TME), and response to drug therapy in various cancers remains unclear. Methods: First, the expression, prognostic value, and genetic and epigenetic alterations of lactylation genes were systematically explored in a pan-cancer manner. Lactylation scores were derived for each tumor using the single-sample gene set enrichment analysis (ssGSEA) algorithm. The correlation of lactylation scores with clinical features, prognosis, and TME was assessed by integrating multiple computational methods. In addition, GSE135222 data was used to assess the efficacy of lactylation scores in predicting immunotherapy outcomes. The expression of lactylation genes in breast cancers and gliomas were verified by RNA-sequencing. Results: Lactylation genes were significantly upregulated in most cancer types. CREBBP and EP300 exhibited high mutation rates in pan-cancer analysis. The prognostic impact of the lactylation score varied by tumor type, and lactylation score was a protective factor for KIRC, ACC, READ, LGG, and UVM, and a risk factor for CHOL, DLBC, LAML, and OV. In addition, a high lactylation score was associated with cold TME. The infiltration levels of CD8+ T, γδT, natural killer T cell (NKT), and NK cells were lower in tumors with higher lactylation scores. Finally, immunotherapy efficacy was worse in patients with high lactylation scores than other types. Conclusion: Lactylation genes are involved in malignancy formation. Lactylation score serves as a promising biomarker for predicting patient prognosis and immunotherapy efficacy.

Bulk anda single-cell transcriptome profiling reveals the molecular characteristics of T cell-mediated tumor killing in pancreatic cancer.

Background: Despite the potential of immune checkpoint blockade (ICB) as a promising treatment for Pancreatic adenocarcinoma (PAAD), there is still a need to identify specific subgroups of PAAD patients who may benefit more from ICB. T cell-mediated tumor killing (TTK) is the primary concept behind ICB. We explored subtypes according to genes correlated with the sensitivity to TKK and unraveled their underlying associations for PAAD immunotherapies. Methods: Genes that control the responsiveness of T cell-induced tumor destruction (GSTTK) were examined in PAAD, focusing on their varying expression levels and association with survival results. Moreover, samples with PAAD were separated into two subsets using unsupervised clustering based on GSTTK. Variability was evident in the tumor immune microenvironment, genetic mutation, and response to immunotherapy among different groups. In the end, we developed TRGscore, an innovative scoring system, and investigated its clinical and predictive significance in determining sensitivity to immunotherapy. Results: Patients with PAAD were categorized into 2 clusters based on the expression of 52 GSTTKs, which showed varying levels and prognostic relevance, revealing unique TTK patterns. Survival outcome, immune cell infiltration, immunotherapy responses, and functional enrichment are also distinguished among the two clusters. Moreover, we found the CATSPER1 gene promotes the progression of PAAD through experiments. In addition, the TRGscore effectively predicted the responses to chemotherapeutics or immunotherapy in patients with PAAD and overall survival. Conclusions: TTK exerted a vital influence on the tumor immune environment in PAAD. A greater understanding of TIME characteristics was gained through the evaluation of the variations in TTK modes across different tumor types. It highlights variations in the performance of T cells in PAAD and provides direction for improved treatment approaches.

Prognostic value analysis of cholesterol and cholesterol homeostasis related genes in breast cancer by Mendelian randomization and multi-omics machine learning.

Introduction: The high incidence of breast cancer (BC) prompted us to explore more factors that might affect its occurrence, development, treatment, and also recurrence. Dysregulation of cholesterol metabolism has been widely observed in BC; however, the detailed role of how cholesterol metabolism affects chemo-sensitivity, and immune response, as well as the clinical outcome of BC is unknown. Methods: With Mendelian randomization (MR) analysis, the potential causal relationship between genetic variants of cholesterol and BC risk was assessed first. Then we analyzed 73 cholesterol homeostasis-related genes (CHGs) in BC samples and their expression patterns in the TCGA cohort with consensus clustering analysis, aiming to figure out the relationship between cholesterol homeostasis and BC prognosis. Based on the CHG analysis, we established a CAG_score used for predicting therapeutic response and overall survival (OS) of BC patients. Furthermore, a machine learning method was adopted to accurately predict the prognosis of BC patients by comparing multi-omics differences of different risk groups. Results: We observed that the alterations in plasma cholesterol appear to be correlative with the venture of BC (MR Egger, OR: 0.54, 95% CI: 0.35-0.84, p<0.006). The expression patterns of CHGs were classified into two distinct groups(C1 and C2). Notably, the C1 group exhibited a favorable prognosis characterized by a suppressed immune response and enhanced cholesterol metabolism in comparison to the C2 group. In addition, high CHG score were accompanied by high performance of tumor angiogenesis genes. Interestingly, the expression of vascular genes (CDH5, CLDN5, TIE1, JAM2, TEK) is lower in patients with high expression of CHGs, which means that these patients have poorer vascular stability. The CAG_score exhibits robust predictive capability for the immune microenvironment characteristics and prognosis of patients(AUC=0.79). It can also optimize the administration of various first-line drugs, including AKT inhibitors VIII Imatinib, Crizotinib, Saracatinib, Erlotinib, Dasatinib, Rapamycin, Roscovitine and Shikonin in BC patients. Finally, we employed machine learning techniques to construct a multi-omics prediction model(Risklight),with an area under the feature curve (AUC) of up to 0.89. Conclusion: With the help of CAG_score and Risklight, we reveal the signature of cholesterol homeostasis-related genes for angiogenesis, immune responses, and the therapeutic response in breast cancer, which contributes to precision medicine and improved prognosis of BC.

Development of a CD8+ T cell-based molecular classification for predicting prognosis and heterogeneity in triple-negative breast cancer by integrated analysis of single-cell and bulk RNA-sequencing.

Background: Triple-negative breast cancer (TNBC), although the most intractable subtype, is characterized by abundant immunogenicity, which enhances responsiveness to immunotherapeutic measures. Methods: First, we identified CD8+ T cell core genes (TRCG) based on single-cell sequence and traditional transcriptome sequencing and then used this data to develop a first-of-its-kind classification system based on CD8+ T cells in patients with TNBC. Next, TRCG-related patterns were systematically analyzed, and their correlation with genomic features, immune activity (microenvironment associated with immune infiltration), and clinicopathological characteristics were assessed in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), the Cancer Genome Atlas (TCGA), GSE103091, GSE96058 databases. Additionally, a CD8+ T cell-related prognostic signature (TRPS) was developed to quantify a patient-specific TRCG pattern. What's more, the genes-related TRPS was validated by polymerase chain reaction (PCR) experiment. Results: This study, for the first time, distinguished two subsets in patients with TNBC based on the TRCG. The immune microenvironment and prognostic stratification between these have distinct heterogeneity. Furthermore, this study constructed a novel scoring system named TRPS, which we show to be a robust prognostic marker for TNBC that is related to the intensity of immune infiltration and immunotherapy. Moreover, the levels of genes related the TRPS were validated by quantitative Real-Time PCR. Conclusions: Consequently, this study unraveled an association between the TRCG and the tumor microenvironment in TNBC. TRPS model represents an effective tool for survival prediction and treatment guidance in TNBC that can also help identify individual variations in TME and stratify patients who are sensitive to anticancer immunotherapy.

ß, ß-Dimethylacrylshikonin potentiates paclitaxel activity, suppresses immune evasion and triple negative breast cancer progression via STAT3Y705 phosphorylation inhibition based on network pharmacology and transcriptomics analysis.

BACKGOUND: Triple negative breast cancer (TNBC) is an extremely aggressive and rapidly progressing cancer, wherein existing therapies provide little benefit to patients. ß, ß-Dimethylacrylshikonin (DMAS), an active naphthoquinone derived from comfrey root, has potent anticancer activity. However, the antitumor function of DMAS against TNBC remains to be proved. PURPOSE: Explore effects of DMAS on TNBC and clarify the mechanism. STUDY DESIGN: Network pharmacology, transcriptomics and various cell functional experiments were applied to TNBC cells to explore the effects of DMAS on TNBC. The conclusions were further validated in xenograft animal models. METHODS: MTT, EdU, transwell, scratch tests, flow cytometry, immunofluorescence, and immunoblot were utilized to assess the activity of DMAS on three TNBC cell lines. The anti-TNBC mechanism of DMAS was clarified by overexpression and knockdown of STAT3 in BT-549 cells. In vivo efficacy of DMAS was analysed using a xenograft mouse model. RESULTS: In vitro analysis revealed that DMAS inhibited the G2/M phase transition and suppressed TNBC proliferation. Additionally, DMAS triggered mitochondrial-dependent apoptosis and reduced cell migration by antagonizing epithelial-mesenchymal transition. Mechanistically, DMAS exerted its antitumour effects by inhibiting STAT3Y705 phosphorylation. STAT3 overexpression abolished the inhibitory effect of DMAS. Further studies showed that treatment with DMAS inhibited TNBC growth in a xenograft model. Notably, DMAS potentiated the sensitivity of TNBC to paclitaxel and inhibited immune evasion by downregulating the immune checkpoint PD-L1. CONCLUSIONS: For the first time, our study revealed that DMAS potentiates paclitaxel activity, suppresses immune evasion and TNBC progression by inhibiting STAT3 pathway. It has the potential as a promising agent for TNBC.

Characterization of chromatin regulators identified prognosis and heterogeneity in hepatocellular carcinoma.

Liver carcinogenesis is a multiprocess that involves complicated interactions between genetics, epigenetics, and transcriptomic alterations. Aberrant chromatin regulator (CR) expressions, which are vital regulatory epigenetics, have been found to be associated with multiple biological processes. Nevertheless, the impression of CRs on tumor microenvironment remodeling and hepatocellular carcinoma (HCC) prognosis remains obscure. Thus, this study aimed to systematically analyze CR-related patterns and their correlation with genomic features, metabolism, cuproptosis activity, and clinicopathological features of patients with HCC in The Cancer Genome Atlas, International Cancer Genome Consortium-LIRI-JP cohort, and GSE14520 that utilized unsupervised consensus clustering. Three CR-related patterns were recognized, and the CRs phenotype-related gene signature (CRsscore) was developed using the least absolute shrinkage and selection operator-Cox regression and multivariate Cox algorithms to represent the individual CR-related pattern. Additionally, the CRsscore was an independent prognostic index that served as a fine predictor for energy metabolism and cuproptosis activity in HCC. Accordingly, describing a wide landscape of CR characteristics may assist us to illustrate the sealed association between epigenetics, energy metabolism, and cuproptosis activity. This study may discern new tumor therapeutic targets and exploit personalized therapy for patients.

A Novel Approach: Combining Prognostic Models and Network Pharmacology to Target Breast Cancer Necroptosis-Associated Genes.

Breast cancer (BC) accounts for the highest proportion of the all cancers among women, and necroptosis is recognized as a form of caspase-independent programmed cell death. We created prognostic signatures using univariate survival analysis, and lasso regression, to assess immune microenvironments between subgroups. We then used network pharmacology to bind our drugs to target differentially expressed genes (DEGs). A signature comprising a set of necroptosis-related genes was established to predict patient outcomes based on median risk scores. Those above and below the median were classified as high-risk group (HRG) and low-risk group (LRG), respectively. Patients at high risk had lower overall survival, and poorer predicted tumor, nodes, and metastases stages (TNM). The novel prognostic signature can effectively predict the prognosis of breast cancer patients docking of ß,ß-dimethyl acryloyl shikonin (DMAS) to possible targets to cure breast cancer. We found that all current prognostic models do not offer suitable treatment options. In additional, by docking drugs DMAS that have been initially validated in our laboratory to treat breast cancer. We hope that this novel approach could contribute to cancer research.

Amino Acid Metabolism-Related lncRNA Signature Predicts the Prognosis of Breast Cancer.

Background and Purpose: Breast cancer (BRCA) is the most frequent female malignancy and is potentially life threatening. The amino acid metabolism (AAM) has been shown to be strongly associated with the development and progression of human malignancies. In turn, long noncoding RNAs (lncRNAs) exert an important influence on the regulation of metabolism. Therefore, we attempted to build an AAM-related lncRNA prognostic model for BRCA and illustrate its immune characteristics and molecular mechanism. Experimental Design: The RNA-seq data for BRCA from the TCGA-BRCA datasets were stochastically split into training and validation cohorts at a 3:1 ratio, to construct and validate the model, respectively. The amino acid metabolism-related genes were obtained from the Molecular Signature Database. A univariate Cox analysis, least absolute shrinkage and selection operator (LASSO) regression, and a multivariate Cox analysis were applied to create a predictive risk signature. Subsequently, the immune and molecular characteristics and the benefits of chemotherapeutic drugs in the high-risk and low-risk subgroups were examined. Results: The prognostic model was developed based on the lncRNA group including LIPE-AS1, AC124067.4, LINC01655, AP005131.3, AC015802.3, USP30-AS1, SNHG26, and AL589765.4. Low-risk patients had a more favorable overall survival than did high-risk patients, in accordance with the results obtained for the validation cohort and the complete TCGA cohort. The elaborate results illustrated that a low-risk index was correlated with DNA-repair-associated pathways; a low TP53 and PIK3CA mutation rate; high infiltration of CD4+ T cells, CD8+ T cells, and M1 macrophages; active immunity; and less-aggressive phenotypes. In contrast, a high-risk index was correlated with cancer and metastasis-related pathways; a high PIK3CA and TP53 mutation rate; high infiltration of M0 macrophages, fibroblasts, and M2 macrophages; inhibition of the immune response; and more invasive phenotypes. Conclusion: In conclusion, we attempted to shed light on the importance of AAM-associated lncRNAs in BRCA. The prognostic model built here might be acknowledged as an indispensable reference for predicting the outcome of patients with BRCA and help identify immune and molecular characteristics.