Comprehensive Analysis of Immunobiological Effects and Prognostic Significance Related to Endoplasmic Reticulum Stress in Pancreatic Carcinoma.

Context: Pancreatic cancer (PC) has a poor response to the many treatments available for it, including surgery, chemotherapeutics, targeted therapy, and immunotherapy. It's crucial to investigate alternative methods of prognostic assessment and decision-making in choosing a therapy, making it necessary to explore its differentially expressed genes (DEGs). Objective: The study intended to assess the role of endoplasmic reticulum stress (ERS)-related genes (ERSRGs) in PC to create an effective, prognostic risk-prediction model and potential immunotherapy options. Design: The research team performed a genetic study. Setting: The study took place at the Affiliated Hospital of Nantong University in Nantong, Jiangsu, China. Outcome Measures: The research team: (1) performed molecular subtype identification and analysis, (2) developed a prognostic risk model, (3) evaluated the clinical features of the risk model, (4) identified the clinicopathological features affecting survival, (5) analyzed the potential immune roles in ERS, (6) constructed five gene signatures associated with ERS, (7) examined the association between different risk categories and sensitivity to GDSC drugs as a potential predictor of response to chemotherapy , and (8) identified the biological processes associated with different risk categories. Results: Significant differences existed in the survival prognosis of subtype C and subtype A or B (P < .001). The high-risk group with the lower TIDE score had a significantly better response to immunotherapy (P < .0057). The high-risk group had a significantly higher somatic mutation rate (P < .00017) and a worse survival prognosis (P < .001). Differences in mRNA expression existed for ERAP2 (P < .001), IGF2BP2 (P = .006), DSG3 (P = .001), MAPK10 (P = .006), and PRKCSH (P ≤ .015) in clinical samples. Conclusions: Through the analysis of ERS subtypes of pancreatic cancer, the study found that the infiltration abundance of stromal cells and immune cells can affected by ERS, thus changing the prognosis of patients. The predictive model provides reference values for clinical prognosis and immunotherapy for PC patients through its ability to evaluate patients' immune statuses. Clinical treatment can provide individualized guidance and can effectively predict the prognosis of PC patients.

RNA-binding motif 4 promotes angiogenesis in HCC by selectively activating VEGF-A expression.

Increased angiogenesis in the liver plays a critical role in the progression of hepatocellular carcinoma (HCC). However, the molecular mechanism underlying increased angiogenesis in HCC is not well understood. Current study was designed to identify the potential angiogenic effect of RNA-binding motif 4 (RBM4)through a small-scale overexpression screening, followed by comparison of the expression level of RBM4 in cancer and adjacent tissues in multiple malignancies to explore the relationship between RBM4 and CD31 protein expression level and related clinical indicators, and understand the role of RBM4 in the hepatocellular carcinoma. To understand the specific mechanism of RBM4 in detail, transcriptome sequencing, mass spectrometry and multiple molecular cytological studies were performed. These cellular level results were verified by experiments in animal models of nude mice. The increased expression of RBM4 in cancer tissues, suggested its use as a prognostic biomarker. The RBM4 expression was found to be strongly correlated with tumor microvessel density. Mechanistically, RBM4 mediated its effects via interaction with HNRNP-M through the latter's WDR15 domain, which then stabilized RelA/p65 mRNA. Consequently, RBM4 induced the activation of the NF-kB signaling pathway, upregulating the expression of proangiogenic factor VEGF-A. The results confirmed the mechanism by which RBM4 promotes angiogenesis in hepatocellular carcinoma suggesting RBM4 as a crucial promoter of angiogenesis in HCC, helping understand regulation of NF-kB signaling in HCC.

Ubiquitin-specific protease 7 is a druggable target that is essential for pancreatic cancer growth and chemoresistance.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers, and most patients die within one year after diagnosis. This cancer is resistant to almost all current therapies, so there is an urgent need to identify novel druggable targets. Ubiquitin-specific protease 7 (USP7) is a deubiquitinase that functions in carcinogenesis, but its role in PDAC is unknown. Our experiments indicated that several subtypes of PDAC cells are sensitive to USP7 inhibition. In particular, pharmaceutical inhibition of USP7 by the small molecule P22077 attenuated PDAC cell growth and induced cell death in vitro and in vivo. Pharmaceutical inhibition of USP7 in P22077-resistant PDAC cells allowed them to overcome chemoresistance. Genetic silencing experiments supported the importance of USP7 in the pathogenesis of PDAC. In particular, genetic disruption of USP7 greatly reduced cell proliferation and chemoresistance in vitro and prevented PDAC growth in vivo. Protein profiling by mass spectrometry (MS) indicated USP7 was associated 4 ontology terms: translation, localization and protein transporting, nucleotide or ribonucleotide binding, and ubiquitin-dependent catabolic processes. Puromycin labeling indicated that P22077 greatly reduced protein synthesis, and transcriptional analysis indicated that P22077 significantly altered the extracellular space matrix. In summary, we provided multiple lines of evidence which indicate that USP7 plays a critical role in PDAC, and may therefore be a suitable target for treatment of this cancer.

Orchestrating a biomarker panel with lncRNAs and mRNAs for predicting survival in pancreatic ductal adenocarcinoma.

The low survival of patients with pancreatic ductal adenocarcinoma (PDAC) makes the treatment of this disease one of the most challenging task in modern medicine. Here, by mining a large-scale cancer genome atlas data set of pancreatic cancer tissues, we identified 21 long noncoding RNAs (lncRNAs) that significantly associated with overall survival in patients with PDAC (P < .01). Further analysis revealed that 8 lncRNAs turned out to be independently correlated with patients' overall survival, and the risk score could be calculated based on their expression. To obtain a better predicting power, we integrated lncRNA data with a total of 410 differently expressed messenger RNAs (mRNAs) screened from PDAC and normal tissues in gene expression omnibus (GEO) database. The integration resulted in a much better panel including 8 lncRNAs (RP3.470B24.5, CTA.941F9.9, RP11.557H15.3, LINC00960, AP000479.1, LINC00635, LINC00636, and AC073133.1) and 8 mRNAs (DHRS9, ONECUT1, OR8D4, MT1M, TCN1, MMP9, DPYSL3, and TTN) to predict prognosis. A functional evaluation showed that these lncRNAs might play roles in pancreatic secretion, cell adhesion, and proteolysis. Using normal and pancreatic cancer cell lines, we confirmed that a majority of identified lncRNAs and mRNAs showed altered expressions in pancreatic cancer cells. Especially, LINC01589, LINC00960, TCN1, and MT1M showed a profoundly increased expression in pancreatic cancer cells, which suggests their potentially important role in pancreatic cancer. The results of our work indicate that lncRNAs have vital roles in PADC and provide new insights to integrate multiple kinds of markers in clinical practices.

RACK1 overexpression associates with pancreatic ductal adenocarcinoma growth and poor prognosis.

OBJECTIVES: The receptor for activated protein kinase C (RACK1) is a scaffold protein involved in multiple intracellular signal pathways. Previous studies have shown that RACK1 is associated with the progression of multiple cancer types, including hepatocellular carcinoma and gastric cancer. However, the role of RACK1 in human pancreatic ductal adenocarcinoma (PDAC) remains unclear. METHODS: In this study, the expression of RACK1 was evaluated by Western blot analysis in 8 paired fresh PDAC tissues and immunohistochemistry on 179 paraffin-embedded slices. Then, we used Fisher exact test to analyze the correlation between RACK1 expression and clinicopathological characteristics. Starvation and re-feeding assay was used to assess cell cycle. Western blot, CCK8, flow cytometry assays, and colony formation analyses demonstrated that RACK1 played an essential role in PDAC development. Annexin-V/PI apoptotic assay and western blot showed that RACK1 was involved in regulating the apoptosis of PDAC cells. RESULTS: RACK1 was highly expressed in PDAC tissues and cell lines and was significantly associated with multiple clinicopathological factors. Univariate and multivariate analyses showed that high RACK1 expression was identified to be an independent prognostic factor for PDAC patients' survival. In vitro, serum starvation-refeeding experiment suggested that RACK1 was upregulated in proliferating PDAC cells, together with the percentage of cells at the S phase, and was correlated with the expression of Cyclin D1. Moreover, Overexpression of RACK1 facilitated the proliferation and cell cycle progression of PDAC cells, while downregulation of RACK1 induced growth impairment, G1/S cell cycle arrest and apoptosis in PDAC cells. Silencing RACK1 decreased bcl-2 expression, increased cleaved caspase3 expression level and induced the apoptosis of PDAC cells. CONCLUSIONS: Our results suggest that RACK1 could play an important role in the tumorigenesis of PDAC and serve as a potential therapeutical target in PDAC treatment.

Acanthopanax versus 3-Methyladenine Ameliorates Sodium Taurocholate-Induced Severe Acute Pancreatitis by Inhibiting the Autophagic Pathway in Rats.

Objectives. To observe the therapeutic effects of Acanthopanax and 3-methyladenine against severe acute pancreatitis (SAP). Methods. Sodium taurocholate-induced SAP rats were equally randomized into a SAP group, an Acanthopanax group, and a 3-methyladenine group. Serum amylase levels were determined by ELISA; protein and mRNA expression levels of nucleus nuclear factor kappa B (NF-κB) p65, light chain 3II (LC3-II), and Beclin-1 and mRNA expression levels of Class III phosphatidylinositol 3-kinase (PI3K-III) in pancreas tissue were detected by Western blot and quantitative real-time PCR, respectively; mortality and pathological change of the pancreas were observed at 3, 12, and 24 h after operation. Results. There was no significant difference in mortality between SAP group and both treatment groups (P > 0.05). Serum amylase levels, protein, and mRNA expression levels of nucleus NF-κB p65, LC3-II, and Beclin-1 protein, mRNA expression levels of PI3K-III, and pathological score of the pancreas in both treatment groups were significantly lower than those in SAP group at 12 and 24 h after operation (P < 0.05 or 0.01). The number of autophagosomes and autophagolysosomes of pancreatic acinar cells in both treatment groups was smaller than that in SAP group at 12 and 24 h. Conclusions. Acanthopanax and 3-methyladenine had similar therapeutic effects against SAP in rats. The mechanism may be through inhibiting abnormal autophagy activation of pancreatic acinar cells.

High expression of ErbB3 binding protein 1 (EBP1) predicts poor prognosis of pancreatic ductal adenocarcinoma (PDAC).

Recent studies have identified that ErbB3 binding protein 1 (EBP1) is broadly expressed in various cancer tissues and critically involved in plenty of biological processes in this regard. However, the functional role of EBP1 in pancreatic ductal adenocarcinoma (PDAC) has never been elucidated. In this study, we found that EBP1 could serve as a prognostic biomarker of PDAC. Western blot analysis revealed that EBP1 was remarkably upregulated in PDAC tissues and cell lines. Using immunohistochemical analysis, we showed that the expression of EBP1 was correlated with tumor size (P = 0.004), histological differentiation (P = 0.041), and tumor node metastasis (TNM) stage (P = 0.000). Notably, Kaplan-Meier curve showed that high expression of EBP1 predicted significantly worsened prognosis of PDAC patients (P = 0.001). In addition, knockdown of EBP1 expression suppressed PDAC cell proliferation and retarded cell cycle progression. Furthermore, depletion of EBP1 induced the apoptosis of Panc-1 cells. Of great interest, we found that EBP1 interacted with anti-apoptotic protein, Bcl-xL, and promoted its accumulation. In summary, our results suggest that EBP1 is a novel prognostic indicator and potential therapeutic target of PDAC, shedding new insights into the important role of EBP1 in cancer development.

NF45 overexpression is associated with poor prognosis and enhanced cell proliferation of pancreatic ductal adenocarcinoma.

NF45, also referred to as nuclear factor of activated T cells, has been reported to promote the progression of multiple cancer types. However, the expression and physiological significance of NF45 in pancreatic ductal adenocarcinoma (PDAC) remain largely elusive. In this study, we investigated the clinical relevance and potential role of NF45 expression in PDAC development. Western blot analysis revealed that NF45 was remarkably upregulated in PDAC tissues, compared with the adjacent non-tumorous ones. In addition, the expression of NF45 in 122 patients with PDAC was evaluated using immunohistochemistry. In this way, we found that NF45 was abundantly expressed in PDAC tissues, and the expression of NF45 was correlated with tumor size (p = 0.007), histological differentiation (p = 0.033), and TNM stage (p = 0.001). Importantly, patients with low levels of NF45 expression exhibited better postoperative prognosis as compared with those with high NF45 expression. Furthermore, using PDAC cell cultures, we found that interference of NF45 expression using siRNA oligos suppressed PDAC cell proliferation and retarded cell cycle progression. Moreover, depletion of NF45 impaired the levels of cellular cyclin E and proliferating cell nuclear antigen (PCNA). Conversely, overexpression of NF45 facilitated the cell growth and accelerated cell cycle progression. Our results establish NF45 as an important indicator of PDAC prognosis with potential utility as a therapeutic target in this lethal disease.

Significance of viral status on recurrence of hepatitis B-related hepatocellular carcinoma after curative therapy: A meta-analysis.

AIM: The impact of viral status on recurrence of hepatitis B-related hepatocellular carcinoma (HCC) after curative therapy remains controversial. This meta-analysis aimed to determine whether the presence of viral load, genotype, specific mutation and antiviral therapy influenced HCC recurrence after curative therapy. METHODS: We performed a meta-analysis including 20 studies to assess the effect of viral status and antiviral therapy with nucleoside analog on recurrence of HCC after curative therapy. The pooled odds ratios (OR) were calculated using a random or fixed effects model. PUBMED, MEDLINE, EMBASE and the Cochrane Database were searched for articles published from 1990 to December 2012. RESULTS: Our results showed that the presence of high viral load significantly increased overall HCC recurrence risk after curative therapy. Pooled data from four studies on the recurrence rate among patients with genotype C infection compared with genotype B showed an increased risk of recurrence. Basal core promoter (BCP) mutation was associated with a significant risk in the recurrence of HCC. The pooled estimate of treatment effect was significantly in favor of a preventive effectiveness of antiviral therapy. CONCLUSION: The present study suggested that HCC patients with high viral load, genotype C and BCP mutation had a significantly higher risk of recurrence. Antiviral therapy has potential beneficial effects after the curative treatment of HCC in terms of tumor recurrence.

Research on the epigenetic modification of pancreatic cancer vaccine.

Pancreatic cancer is characterized as a type of gastrointestinal tumor with a poor prognosis and high degree of malignancy. CIITA gene was found highly methylated in pancreatic carcinoma cell line PANC-1 and responsible for the low expression of MHC-II that may lead to immune evasion. Here, we tried to prepare pancreatic cancer vaccine with PANC-1 cells via epigenetic modification to enhance the MHC-II expression. Then the vaccine was injected into C57BL/6J mice and the effect was examined. Our study found that the vaccine could promote the proliferation of antigen-specific T cells, enhance the killing activity of cytotoxic lymphocytes (CTL), promote Th1-type cells mediated secretion of cytokines IFN-gamma and IL-2 while inhibiting Th2-type cells mediated secretion of IL-4, and inhibit the secretion of TGF-beta. Generally, the epigenetically modified vaccine could enhance the body's anti-tumor immune response, providing feasibility research on cancer vaccine for therapy of pancreatic cancer.

Purse-string suture with nylon cords and metal clips for the treatment of duodenal fistulae under the endoscope: a case report.

Purse-string suture with nylon cords and metal clips under the endoscope is a novel therapeutic technique which is minimally invasive and it is particularly indicated for the closure and repair of gastrointestinal fistula or perforations such as duodenal fistulae. Duodenal fistulae are often caused by medical manipulation, disease progression or trauma. Once this occurs, it leads to a series of pathophysiologic changes and a variety of complications. In most cases, these complications will exacerbate the damage to the organism, and the complications are difficult to treat and can lead to infections, nutrient loss, multi-organ dysfunction and many other adverse effects. In this case report, the use of endoscopic nylon cords combined with purse-string suture and metal clips in the treatment of duodenal fistula is presented and discussed. The patient was treated with endoscopic purse-string suture and the duodenal fistula was significantly improved. The results indicate that endoscopic purse-string suture is an effective strategy for the treatment of duodenal fistulae.

Mechanism of Nicotiflorin in San-Ye-Qing rhizome for anti-inflammatory effect in ulcerative colitis.

BACKGROUND: The incidence of ulcerative colitis (UC) is on the rise globally and the development of drugs targeting UC is urgent. Finding the target of action of natural products is important for drug discovery, elucidation of drug action mechanism, and disease mechanism. San-Ye-Qing (SYQ), is an ancient herbal medicine, but whether the powder of its rhizome has pharmacological effects against UC and its mechanism of action are not clear. PURPOSE: To evaluate the therapeutic effectiveness of rhizome powder of SYQ in treating UC, and conduct an isolation and characterization of the chemical constituents of the powder. Further, screen the most potent compounds among them and determine the potential mechanism for treating UC. METHODS: In vivo, the therapeutic effect of SYQ's rhizome powder on UC was assessed by mice's body weight, DAI score, colon length, tissue MPO activity, serum inflammatory markers, etc. Additionally, HPLC was used to isolate and identify the specific chemical components of SYQ's rhizome powder. Then, the most effective compounds and their therapeutic targets were analysed and screened in SYQ rhizome powder using network pharmacology, combined with CCK-8 assay, NO release assay and molecular docking assay, in conjunction with CETSA, DARTS, SPR and enzyme activity assay. Finally, the biological effects of the key compound on the targets were validated using Western blot and ELISA. RESULTS: In vivo, SYQ rhizome powder effectively restored mice's body weight, lowered DAI and pathological score, downregulated the expression of inflammatory biomarkers, and restored colon length, as well as the colonic epithelial and mucus barriers. Afterward, 9 compounds were isolated and identified from the powder of the rhizomes of SYQ by HPLC. Nicotiflorin is the primary compound in SYQ with the highest concentration. According to both CCK-8 and NO release tests, Nicotiflorin is also the most efficacious compound. Combined with network pharmacological prediction, molecular docking analysis, CETSA, DARTS, SPR and enzyme activity assay, Nicotiflorin may ultimately suppress inflammation by targeting p65 and inhibiting the NF-κB pathway, thereby attenuating the activation of NLRP3 inflammasome. To verify this conclusion, Western blot and ELISA experiments were conducted. CONCLUSIONS: Our results suggest that the extract from SYQ rhizomes has therapeutic properties for UC. Its active ingredient Nicotiflorin exerted potent anti-UC effects by binding to p65 and inhibiting the activation of NF-κB and NLRP3 inflammasomes.

High-Accuracy Tomato Leaf Disease Image-Text Retrieval Method Utilizing LAFANet.

Tomato leaf disease control in the field of smart agriculture urgently requires attention and reinforcement. This paper proposes a method called LAFANet for image-text retrieval, which integrates image and text information for joint analysis of multimodal data, helping agricultural practitioners to provide more comprehensive and in-depth diagnostic evidence to ensure the quality and yield of tomatoes. First, we focus on six common tomato leaf disease images and text descriptions, creating a Tomato Leaf Disease Image-Text Retrieval Dataset (TLDITRD), introducing image-text retrieval into the field of tomato leaf disease retrieval. Then, utilizing ViT and BERT models, we extract detailed image features and sequences of textual features, incorporating contextual information from image-text pairs. To address errors in image-text retrieval caused by complex backgrounds, we propose Learnable Fusion Attention (LFA) to amplify the fusion of textual and image features, thereby extracting substantial semantic insights from both modalities. To delve further into the semantic connections across various modalities, we propose a False Negative Elimination-Adversarial Negative Selection (FNE-ANS) approach. This method aims to identify adversarial negative instances that specifically target false negatives within the triplet function, thereby imposing constraints on the model. To bolster the model's capacity for generalization and precision, we propose Adversarial Regularization (AR). This approach involves incorporating adversarial perturbations during model training, thereby fortifying its resilience and adaptability to slight variations in input data. Experimental results show that, compared with existing ultramodern models, LAFANet outperformed existing models on TLDITRD dataset, with top1, top5, and top10 reaching 83.3% and 90.0%, and top1, top5, and top10 reaching 80.3%, 93.7%, and 96.3%. LAFANet offers fresh technical backing and algorithmic insights for the retrieval of tomato leaf disease through image-text correlation.

Identification of Pepper Leaf Diseases Based on TPSAO-AMWNet.

Pepper is a high-economic-value agricultural crop that faces diverse disease challenges such as blight and anthracnose. These diseases not only reduce the yield of pepper but, in severe cases, can also cause significant economic losses and threaten food security. The timely and accurate identification of pepper diseases is crucial. Image recognition technology plays a key role in this aspect by automating and efficiently identifying pepper diseases, helping agricultural workers to adopt and implement effective control strategies, alleviating the impact of diseases, and being of great importance for improving agricultural production efficiency and promoting sustainable agricultural development. In response to issues such as edge-blurring and the extraction of minute features in pepper disease image recognition, as well as the difficulty in determining the optimal learning rate during the training process of traditional pepper disease identification networks, a new pepper disease recognition model based on the TPSAO-AMWNet is proposed. First, an Adaptive Residual Pyramid Convolution (ARPC) structure combined with a Squeeze-and-Excitation (SE) module is proposed to solve the problem of edge-blurring by utilizing adaptivity and channel attention; secondly, to address the issue of micro-feature extraction, Minor Triplet Disease Focus Attention (MTDFA) is proposed to enhance the capture of local details of pepper leaf disease features while maintaining attention to global features, reducing interference from irrelevant regions; then, a mixed loss function combining Weighted Focal Loss and L2 regularization (WfrLoss) is introduced to refine the learning strategy during dataset processing, enhancing the model's performance and generalization capabilities while preventing overfitting. Subsequently, to tackle the challenge of determining the optimal learning rate, the tent particle snow ablation optimizer (TPSAO) is developed to accurately identify the most effective learning rate. The TPSAO-AMWNet model, trained on our custom datasets, is evaluated against other existing methods. The model attains an average accuracy of 93.52% and an F1 score of 93.15%, demonstrating robust effectiveness and practicality in classifying pepper diseases. These results also offer valuable insights for disease detection in various other crops.

A Precise Framework for Rice Leaf Disease Image-Text Retrieval Using FHTW-Net.

Cross-modal retrieval for rice leaf diseases is crucial for prevention, providing agricultural experts with data-driven decision support to address disease threats and safeguard rice production. To overcome the limitations of current crop leaf disease retrieval frameworks, we focused on four common rice leaf diseases and established the first cross-modal rice leaf disease retrieval dataset (CRLDRD). We introduced cross-modal retrieval to the domain of rice leaf disease retrieval and introduced FHTW-Net, a framework for rice leaf disease image-text retrieval. To address the challenge of matching diverse image categories with complex text descriptions during the retrieval process, we initially employed ViT and BERT to extract fine-grained image and text feature sequences enriched with contextual information. Subsequently, two-way mixed self-attention (TMS) was introduced to enhance both image and text feature sequences, with the aim of uncovering important semantic information in both modalities. Then, we developed false-negative elimination-hard negative mining (FNE-HNM) strategy to facilitate in-depth exploration of semantic connections between different modalities. This strategy aids in selecting challenging negative samples for elimination to constrain the model within the triplet loss function. Finally, we introduced warm-up bat algorithm (WBA) for learning rate optimization, which improves the model's convergence speed and accuracy. Experimental results demonstrated that FHTW-Net outperforms state-of-the-art models. In image-to-text retrieval, it achieved R@1, R@5, and R@10 accuracies of 83.5%, 92%, and 94%, respectively, while in text-to-image retrieval, it achieved accuracies of 82.5%, 98%, and 98.5%, respectively. FHTW-Net offers advanced technical support and algorithmic guidance for cross-modal retrieval of rice leaf diseases.

[Corrigendum] FKBP11 protects intestinal epithelial cells against inflammation­induced apoptosis via the JNK­caspase pathway in Crohn's disease.

Subsequently to the publication of the above article, the authors realized that Fig. 4 in their paper had been assembled containing two erroneously placed gel slices; essentially, the GAPDH bands featured in Fig. 4A had also been included in Fig. 5, and the data for the FKBP11 bands in Fig. 4A had also been included to show the GRP78 bands in Fig. 4. The authors were able to revisit their original data and to correct the data that had been featured incorrectly in Fig. 4. The corrected version of Fig. 4, now showing the true data for the GRP78 protein bands in Fig. 4C and the correct GAPDH protein bands for Fig. 4A, is shown on the next page. Note that these errors did not significantly affect the results or the conclusions reported in this paper. All the authors agree to the publication of this Corrigendum, and are grateful to the Editor of Molecular Medicine Reports for allowing them the opportunity to correct this error. Moreover, the authors apologize to the readership for any inconvenience caused. [Molecular Medicine Reports 18: 4428­4438, 2018; DOI: 10.3892/mmr.2018.9485].

IMT030122, A novel engineered EpCAM/CD3/4-1BB tri-specific antibody, enhances T-cell recruitment and demonstrates anti-tumor activity in mouse models of colorectal cancer.

Colorectal cancer is a major global health burden, with limited efficacy of traditional treatment modalities in improving survival rates. However, recently advances in immunotherapy has improved treatment outcomes for patients with this cancer. To address the continuing need for improved treatment efficacy, this study introduced a novel tri-specific antibody, IMT030122, that targets EpCAM, 4-1BB, and CD3. We evaluated the pharmacological efficacy and mechanism of action of IMT030122 in vitro and in vivo. In in vitro studies, IMT030122 exhibited differential binding to antigens and cells expressing EpCAM, 4-1BB, and CD3. Moreover, IMT030122 relied on EpCAM-targeted activation of intracellular CD3 and 4-1BB signaling and mediated T cell cytotoxicity specific to HCT116 colorectal cancer cells. In vivo, IMT030122 demonstrated potent anti-tumor activity, significantly inhibiting the growth of colon cancer HCT116 and MC38-hEpCAM subcutaneous grafts. Further pharmacological analysis revealed that IMT030122 recruited lymphocytes from peripheral blood into colorectal cancer tissue and exerted durable anti-tumor activity, predominantly by promoting the activation, proliferation, and differentiation of CD8T cells. Notably, IMT030122 still exhibited anti-tumor efficacy even in the presence of significantly depleted lymphocytes in colorectal cancer tissue. The potent pharmacological activity and anti-tumor effects of IMT030122 suggest it may enhance treatment efficacy and substantially extend the survival of patients with colorectal cancer in the future.

Artificial intelligence predicts direct-acting antivirals failure among hepatitis C virus patients: A nationwide hepatitis C virus registry program.

BACKGROUND/AIMS: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1-3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. METHODS: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. RESULTS: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. CONCLUSION: Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.

An algorithm for simplified hepatitis C virus treatment with non-specialist care based on nation-wide data from Taiwan.

BACKGROUND: Both European Association for the Study of the Liver (EASL) and American Association for the Study of Liver Diseases and the Infectious Diseases Society of America (AASLD-IDSA) guidelines recommend simplified hepatitis C virus (HCV) treatment with pan-genotypic sofosbuvir/velpatasvir or glecaprevir/pibrentasvir for eligible patients. This observational study used real-world data to assess these regimens' safety in eligible patients and develop an algorithm to identify patients suitable for simplified treatment by non-specialists. METHODS: 7,677 HCV-infected patients from Taiwan Hepatitis C Registry (TACR) who received at least one dose of sofosbuvir/velpatasvir or glecaprevir/pibrentasvir, and fulfilled the EASL/AASLD-IDSA criteria for simplified treatment were analyzed. Multivariate analysis was conducted on patient characteristics and safety data. RESULTS: Overall, 92.8% (7,128/7,677) of patients achieved sustained virological response and only 1.9% (146/7,677) experienced Grades 2-4 laboratory abnormalities in key liver function parameters (alanine aminotransferase, aspartate aminotransferase, and total bilirubin), with only 18 patients (0.23%) experiencing Grades 3-4 abnormalities. Age > 70 years old, presence of hepatocellular carcinoma, total bilirubin > 1.2 mg/dL, estimated glomerular filtration rate < 60 mL/min/1.73 m2, and Fibrosis-4 > 3.25 were associated with higher risks of Grades 2-4 abnormalities. Patients with any of these had an odds of 4.53 times than that of those without in developing Grades 2-4 abnormalities (p < 0.01). CONCLUSIONS: Real-world data from Taiwan confirmed that simplified HCV treatment for eligible patients with pan-genotypic regimens is effective and well tolerated. The TACR algorithm, developed based on this study's results, can further identify patients who can be safely managed by non-specialist care.

Overexpressed nuclear BAG-1 in human hepatocellular carcinoma is associated with poor prognosis and resistance to doxorubicin.

Bcl-2-associated athanogene-1 (BAG-1) is a multifunctional anti-apoptotic protein which regulates an array of cellular processes, including apoptosis, signaling, proliferation, transcription, and cell motility and has been reported to be over-expressed in a number of human malignancies. To investigate the possible involvement of BAG-1 in tumorigenesis of hepatocellular carcinoma (HCC), we performed Western blot analysis in eight paired samples of HCC and adjacent peritumoral tissues and immunohistochemistry in 65 paraffin sections of HCC, which both showed an enhanced expression of nuclear BAG-1 isoform in HCC tissues. Statistical analysis confirmed that overexpression of nuclear BAG-1 in HCC tissues was significantly associated with histological grading (P < 0.001), poor prognosis (P = 0.004), and was found to be an independent prognostic indicator for HCC (P = 0.023). We also noted that BAG-1 was overexpressed in four HCC cell lines compared with a normal hepatocyte cell line, and BAG-1 overexpression increased resistance of HCC cells to doxorubicin, a common chemotherapeutic agent for HCC. Furthermore, we observed that knock down of BAG-1 with siRNA in HepG2 cells increased the chemosensitivity of cells, a process mediated through inhibition of doxorubicin-triggered NF-κB activation; and knock down of BAG-1 suppressed proliferation and cell cycle transition of HepG2 cells. In consequence, our results for the first time indicated that BAG-1 was dysregulated in HCC and suppression of BAG-1 expression which resulted in inhibiting of NF-κB signaling might be developed into a new strategy in HCC therapy.