Engineering Human Pancreatic RNase 1 as an Immunotherapeutic Agent for Cancer Therapy Through Computational and Experimental Studies.

Most plant and bacterial toxins are highly immunogenic with non-specific toxic effects. Human ribonucleases are thought to provide a promising basis for reducing the toxic agent's immunogenic properties, which are candidates for cancer therapy. In the cell, the ribonuclease inhibitor (RI) protein binds to the ribonuclease enzyme and forms a tight complex. This study aimed to engineer and provide a gene construct encoding an improved version of Human Pancreatic RNase 1 (HP-RNase 1) to reduce connection to RI and modulate the immunogenic effects of immunotoxins. To further characterize the interaction complex of HP-RNase 1 and RI, we established various in silico and in vitro approaches. These methods allowed us to specifically monitor interactions within native and engineered HP-RNase 1/RI complexes. In silico research involved molecular dynamics (MD) simulations of native and mutant HP-RNase 1 in their free form and when bound to RI. For HP-RNase 1 engineering, we designed five mutations (K8A/N72A/N89A/R92D/E112/A) based on literature studies, as this combination proved effective for the intended investigation. Then, the cDNA encoding HP-RNase 1 was generated by RT-PCR from blood and cloned into the pSYN2 expression vector. Consequently, wild-type and the engineered HP-RNase 1 were over-expressed in E. coli TG1 and purified using an IMAC column directed against a poly-his tag. The protein products were detected by SDS-PAGE and Western blot analysis. HP-RNase 1 catalytic activity, in the presence of various concentrations of RI, demonstrated that the mutated version of the protein is able to escape the ribonuclease inhibitor and target the RNA substrate 2.5 folds more than that of the wild type. From these data, we tend to suggest the engineered recombinant HP-RNase 1 potentially as a new immunotherapeutic agent for application in human cancer therapy.

The prognostic, diagnostic, and therapeutic impact of Long noncoding RNAs in gastric cancer.

Gastric cancer (GC), ranking as the third deadliest cancer globally, faces challenges of late diagnosis and limited treatment efficacy. Long non-coding RNAs (lncRNAs) emerge as valuable treasured targets for cancer prognosis, diagnosis, and therapy, given their high specificity, convenient non-invasive detection in body fluids, and crucial roles in diverse physiological and pathological processes. Research indicates the significant involvement of lncRNAs in various aspects of GC pathogenesis, including initiation, metastasis, and recurrence, underscoring their potential as novel diagnostic and prognostic biomarkers, as well as therapeutic targets for GC. Despite existing challenges in the clinical application of lncRNAs in GC, the evolving landscape of lncRNA molecular biology holds promise for advancing the survival and treatment outcomes of gastric cancer patients. This review provides insights into recent studies on lncRNAs in gastric cancer, elucidating their molecular mechanisms and exploring the potential clinical applications in GC.

Potential Role of Zinc Finger 365 rs10822013 and rs10995190 in Mammographic Density, Sporadic Breast Cancer Risk, and Prognosis.

Background: Despite suggesting many genetic risk markers as the outcome of Genome-wide association studies (GWAS) for breast cancer, replicating the results in different populations has remained the main issue. In this regard, this study assessed the association of two variations in Zinc Finger 365 (ZNF365) in an Iranian population. Methods: In a case-control study conducted at Mashhad University of Medical Sciences, Mashhad, Iran, between 2017 and 2020, ZNF365-rs10822013 and rs10995190 were genotyped using Allele-Specific PCR (AS-PCR). Breast density was assessed using mammography images. PHASE software module version 2 and SPSS version 16.0 were used for haplotype and statistical analyses. Quantitative and qualitative variables were compared between groups using independent t tests and Chi square tests, respectively. Binary logistic regression analysis was performed to calculate odds ratios. Multivariate analysis was then undertaken for the baseline variables, with a P<0.05 in the univariate analysis. The survival analysis was performed using the Kaplan-Meier method and the log-rank test. Results: In this survey, 732 females, including 342 breast cancer patients and 390 healthy subjects, were enrolled. rs10822013-T allele (P=0.014), rs10995190-G allele (P=0.003), and TG haplotype (P=0.002) were significantly associated with the increased risk of breast cancer. Moreover, rs10995190-GG genotype (P=0.042) and C-G haplotype (P=0.019) revealed a significant association with better overall survival. However, considered polymorphisms and their haplotypes indicated no association with breast density and clinical features of breast cancer. Conclusion: ZNF365 variants might be a potential risk marker of breast cancer in the Iranian population. The interaction between alleles in haplotypes may modulate the amount of the risk conferred by these variants. Further studies on different ethnic groups can validate these results.

Down regulation of Cathepsin W is associated with poor prognosis in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is associated with a very poor prognosis. Therefore, there has been a focus on identifying new biomarkers for its early diagnosis and the prediction of patient survival. Genome-wide RNA and microRNA sequencing, bioinformatics and Machine Learning approaches to identify differentially expressed genes (DEGs), followed by validation in an additional cohort of PDAC patients has been undertaken. To identify DEGs, genome RNA sequencing and clinical data from pancreatic cancer patients were extracted from The Cancer Genome Atlas Database (TCGA). We used Kaplan-Meier analysis of survival curves was used to assess prognostic biomarkers. Ensemble learning, Random Forest (RF), Max Voting, Adaboost, Gradient boosting machines (GBM), and Extreme Gradient Boosting (XGB) techniques were used, and Gradient boosting machines (GBM) were selected with 100% accuracy for analysis. Moreover, protein-protein interaction (PPI), molecular pathways, concomitant expression of DEGs, and correlations between DEGs and clinical data were analyzed. We have evaluated candidate genes, miRNAs, and a combination of these obtained from machine learning algorithms and survival analysis. The results of Machine learning identified 23 genes with negative regulation, five genes with positive regulation, seven microRNAs with negative regulation, and 20 microRNAs with positive regulation in PDAC. Key genes BMF, FRMD4A, ADAP2, PPP1R17, and CACNG3 had the highest coefficient in the advanced stages of the disease. In addition, the survival analysis showed decreased expression of hsa.miR.642a, hsa.mir.363, CD22, BTNL9, and CTSW and overexpression of hsa.miR.153.1, hsa.miR.539, hsa.miR.412 reduced survival rate. CTSW was identified as a novel genetic marker and this was validated using RT-PCR. Machine learning algorithms may be used to Identify key dysregulated genes/miRNAs involved in the disease pathogenesis can be used to detect patients in earlier stages. Our data also demonstrated the prognostic and diagnostic value of CTSW in PDAC.

Identification of ZMYND19 as a novel biomarker of colorectal cancer: RNA-sequencing and machine learning analysis.

Colorectal cancer (CRC) is the third most common cause of cancer-related deaths. The five-year relative survival rate for CRC is estimated to be approximately 90% for patients diagnosed with early stages and 14% for those diagnosed at an advanced stages of disease, respectively. Hence, the development of accurate prognostic markers is required. Bioinformatics enables the identification of dysregulated pathways and novel biomarkers. RNA expression profiling was performed in CRC patients from the TCGA database using a Machine Learning approach to identify differential expression genes (DEGs). Survival curves were assessed using Kaplan-Meier analysis to identify prognostic biomarkers. Furthermore, the molecular pathways, protein-protein interaction, the co-expression of DEGs, and the correlation between DEGs and clinical data have been evaluated. The diagnostic markers were then determined based on machine learning analysis. The results indicated that key upregulated genes are associated with the RNA processing and heterocycle metabolic process, including C10orf2, NOP2, DKC1, BYSL, RRP12, PUS7, MTHFD1L, and PPAT. Furthermore, the survival analysis identified NOP58, OSBPL3, DNAJC2, and ZMYND19 as prognostic markers. The combineROC curve analysis indicated that the combination of C10orf2 -PPAT- ZMYND19 can be considered as diagnostic markers with sensitivity, specificity, and AUC values of 0.98, 1.00, and 0.99, respectively. Eventually, ZMYND19 gene was validated in CRC patients. In conclusion, novel biomarkers of CRC have been identified that may be a promising strategy for early diagnosis, potential treatment, and better prognosis.

The circadian clock as a potential biomarker and therapeutic target in pancreatic cancer.

Pancreatic cancer (PC) has a very high mortality rate globally. Despite ongoing efforts, its prognosis has not improved significantly over the last two decades. Thus, further approaches for optimizing treatment are required. Various biological processes oscillate in a circadian rhythm and are regulated by an endogenous clock. The machinery controlling the circadian cycle is tightly coupled with the cell cycle and can interact with tumor suppressor genes/oncogenes; and can therefore potentially influence cancer progression. Understanding the detailed interactions may lead to the discovery of prognostic and diagnostic biomarkers and new potential targets for treatment. Here, we explain how the circadian system relates to the cell cycle, cancer, and tumor suppressor genes/oncogenes. Furthermore, we propose that circadian clock genes may be potential biomarkers for some cancers and review the current advances in the treatment of PC by targeting the circadian clock. Despite efforts to diagnose pancreatic cancer early, it still remains a cancer with poor prognosis and high mortality rates. While studies have shown the role of molecular clock disruption in tumor initiation, development, and therapy resistance, the role of circadian genes in pancreatic cancer pathogenesis is not yet fully understood and further studies are required to better understand the potential of circadian genes as biomarkers and therapeutic targets.

Mechanism-based Pharmacological Management of Chemotherapy-induced Neuropathic Pain from Preclinical Studies to Clinical Prospective: Platinum-based Drugs, Taxanes, and Vinca Alkaloids.

BACKGROUND: Chemotherapy-induced peripheral neuropathy (CIPN) is a painful condition, experienced by patients undergoing chemotherapy with some specific drugs, such as platinum-based agents, taxanes, and vinca alkaloids. Painful CIPN may lead to dose interruptions and discontinuation of chemotherapy and can negatively impact on the quality of life and clinical outcome of these patients. Due to a lack of a practical medical therapy for CIPN, it is necessary to further explore and identify novel therapeutic options. METHODS: We have reviewed PubMed and EMBASE libraries to gather data on the mechanism-based pharmacological management of chemotherapy-induced neuropathic pain. RESULTS: This review has focused on the potential mechanisms by which these chemotherapeutic agents may be involved in the development of CIPN, and explains how this may be translated into clinical management. Additionally, we have presented an overview of emerging candidates for the prevention and treatment of CIPN in preclinical and clinical studies. CONCLUSION: Taken together, due to the debilitating consequences of CIPN for the quality of life and clinical outcome of cancer survivors, future studies should focus on identifying underlying mechanisms contributing to CIPN as well as developing effective pharmacological interventions based on these mechanistic insights.

Genetic contribution of caspase-8 variants and haplotypes to breast cancer risk and prognosis: a case-control study in Iran.

PURPOSE: Multiple genome-wide and candidate-gene association studies have been conducted to search for common risk variants of breast cancer. Recent large meta-analyses and consolidating evidence have highlighted the role of the caspase-8 gene in breast cancer pathogenesis. Therefore, this study aimed to identify common variations and haplotypes associated with risk and overall survival of breast cancer with respect to underlying susceptibility variants in the CASP8 gene region in a group of the Iranian population. METHODS: In a case-control study with a total of 1008 samples (455 cases and 553 controls), genotyping of 12 candidate polymorphisms, consisting of rs3834129, rs2037815, rs7608692, rs12990906, rs3769821, rs6435074, rs3754934, rs3817578, rs10931936, rs1045485, rs1045487, and rs13113, were performed using PCR-based methods, including ARMS-PCR, AS-PCR, RFLP-PCR, HRM-PCR, and TaqMan-PCR. RESULTS: rs3834129, rs3754934, rs12990906, and rs10931936 were associated with the risk and overall survival of breast cancer. Several haplotypes were also identified an associated with a higher risk of breast cancer, including a three-SNP haplotype rs3817578-rs10931936-rs1045485 [p < 0.001, OR = 1.78(1.32-2.41)]. rs3754934-C allele showed an association with a lower risk of death in all patients [p = 0.022; HR = 0.46(0.23-0.89)] and in the hormone-receptor-positive group [p = 0.038; HR = 0.37(0.14-0.95)], as well as CC genotype in the hormone-receptor-positive group [p = 0.002; HR = 0.09(0.02-0.43)]. CONCLUSION: The present study suggests a diagnostic and prognostic role of CASP8 gene variations in breast cancer. The risky haplotypes are likely to have one or more underlying breast cancer susceptibility alleles. Understanding the mode of action of these alleles will aid individual-level risk prediction. It also may help identify at-risk patients to provide them with better surveillance.

Machine learning algorithms reveal potential miRNAs biomarkers in gastric cancer.

Gastric cancer is the high mortality rate cancers globally, and the current survival rate is 30% even with the use of combination therapies. Recently, mounting evidence indicates the potential role of miRNAs in the diagnosis and assessing the prognosis of cancers. In the state-of-art research in cancer, machine-learning (ML) has gained increasing attention to find clinically useful biomarkers. The present study aimed to identify potential diagnostic and prognostic miRNAs in GC with the application of ML. Using the TCGA database and ML algorithms such as Support Vector Machine (SVM), Random Forest, k-NN, etc., a panel of 29 was obtained. Among the ML algorithms, SVM was chosen (AUC:88.5%, Accuracy:93% in GC). To find common molecular mechanisms of the miRNAs, their common gene targets were predicted using online databases such as miRWalk, miRDB, and Targetscan. Functional and enrichment analyzes were performed using Gene Ontology (GO) and Kyoto Database of Genes and Genomes (KEGG), as well as identification of protein-protein interactions (PPI) using the STRING database. Pathway analysis of the target genes revealed the involvement of several cancer-related pathways including miRNA mediated inhibition of translation, regulation of gene expression by genetic imprinting, and the Wnt signaling pathway. Survival and ROC curve analysis showed that the expression levels of hsa-miR-21, hsa-miR-133a, hsa-miR-146b, and hsa-miR-29c were associated with higher mortality and potentially earlier detection of GC patients. A panel of dysregulated miRNAs that may serve as reliable biomarkers for gastric cancer were identified using machine learning, which represents a powerful tool in biomarker identification.

Circulating Tumor Cells and Cell-free Nucleic Acids as Biomarkers in Colorectal Cancer.

Colorectal cancer (CRC) is currently the second most prevalent cancer diagnosed in women and the third most common kind of cancer in men. Despite tremendous efforts and advancements in diagnostic approaches and treatment options, the mortality rate of CRC accounts for around one million each year globally. The five-year survival rate of CRC is reported to be approximately 14 percent for patients diagnosed at an advanced stage. Due to its significant associated mortality and morbidity, diagnostic tools to identify the disease at its early stages are urgently required. Early diagnosis may lead to better outcomes. The gold standard approach for CRC diagnosis is colonoscopy with biopsy. However, it is an invasive process with a risk of complications and discomfort for the patient. Moreover, it is usually performed in symptomatic or high-risk individuals and therefore, asymptomatic patients might be missed. Thus, alternative non-invasive diagnostic techniques are required to improve CRC outcomes. The new era of personalized medicine is identifying novel biomarkers associated with overall survival and clinical outcomes. Recently, liquid biopsy, a minimally invasive analysis of body fluid biomarkers, has gained attention for diagnosis, evaluation of prognosis, and follow-up of patients with CRC. Several previous studies have demonstrated that this novel approach allows for better understanding of CRC tumor biology and leads to an improvement in clinical outcomes. Here, we explain the enrichment and detection methods of circulating biomarkers, including CTCs, ctDNA, miRNA, lncRNA, and circRNA. Furthermore, we provide an overview on their clinical potential as diagnostic, prognostic, and predictive biomarkers for CRC.

Integrated analysis of multi-omics data for the discovery of biomarkers and therapeutic targets for colorectal cancer.

The considerable burden of colorectal cancer and the rising trend in young adults emphasize the necessity of understanding its underlying mechanisms, providing new diagnostic and prognostic markers, and improving therapeutic approaches. Precision medicine is a new trend all over the world and identification of novel biomarkers and therapeutic targets is a step forward towards this trend. In this context, multi-omics data and integrated analysis are being investigated to develop personalized medicine in the management of colorectal cancer. Given the large amount of data from multi-omics approach, data integration and analysis is a great challenge. In this Review, we summarize how statistical and machine learning techniques are applied to analyze multi-omics data and how it contributes to the discovery of useful diagnostic and prognostic biomarkers and therapeutic targets. Moreover, we discuss the importance of these biomarkers and therapeutic targets in the clinical management of colorectal cancer in the future. Taken together, integrated analysis of multi-omics data has great potential for finding novel diagnostic and prognostic biomarkers and therapeutic targets, however, there are still challenges to overcome in future studies.

Therapeutic Potential of Herbal Medicine against Non-alcoholic Fatty Liver Disease.

Non-alcoholic fatty liver disease (NAFLD) is a common metabolic disorder associated with obesity, diabetes mellitus, dyslipidemia, and cardiovascular disease. A "multiple hit" model has been a widely accepted explanation for the disease's complicated pathogenesis. Despite advances in our knowledge of the processes underlying NAFLD, no conventional pharmaceutical therapy exists. The only currently approved option is to make lifestyle modifications, such as dietary and physical activity changes. The use of medicinal plants in the treatment of NAFLD has recently gained interest. Thus, we review the current knowledge about these agents based on clinical and preclinical studies. Moreover, the association between NAFLD and colorectal cancer (CRC), one of the most common and lethal malignancies, has recently emerged as a new study area. We overview the shared dysregulated pathways and the potential therapeutic effect of herbal medicines for CRC prevention in patients with NAFLD.

The therapeutic potential value of Cancer-testis antigens in immunotherapy of gastric cancer.

Gastric cancer (GC) is the fourth most common cause of mortality and the fifth for incidence, globally. Diagnosis, early prognosis, and therapy remains challenging for this condition, and new tumor-associated antigens are required for its detection and immunotherapy. Cancer-testis antigens (CTAs) are a subfamily of tumor-associated antigens (TAAs) that have been identified as potential biomarkers and targets for cancer immunotherapy. The CTAs-restricted expression pattern in tumor cells and their potential immunogenicity identify them as attractive target candidates in CTA-based diagnosis or prognosis or immunotherapy. To date, numerous studies have reported the dysregulation of CTAs in GC. Several clinical trials have been done to assess CTA-based immunotherapeutic potential in the treatment of GC patients. NY-ESO-1, MAGE, and KK-LC-1 have been used in GC clinical trials. We review recent studies that have investigated the potential of the CTAs in GC regarding the expression, function, aggressive phenotype, prognosis, and immunological responses as well as their possible clinical significance as immunotherapeutic targets with a focus on challenges and future interventions.

Metabolic Pathways Regulating Colorectal Cancer: A Potential Therapeutic Approach.

Colorectal cancer (CRC) is one of the most prevalent cancers globally. Despite recent progress in identifying etiologies and molecular genetics as well as new therapeutic approaches, the clinical outcome of current CRC therapies remains poor. This fact highlights the importance of further understanding underlying mechanisms involved in colorectal tumor initiation and progression. Abnormal metabolic alterations offer an evolutional advantage for CRC tumor cells and enhance their aggressive phenotype. Therefore, dysregulation of cellular metabolism is intricately associated with colorectal tumorigenesis. This review summarizes recent findings regarding the CRC-related changes in cellular metabolic pathways such as glycolysis, tricarboxylic acid cycle, fatty acid oxidation, and mitochondrial metabolism. We describe the oncogenic signaling pathways associated with metabolic dysregulation during malignant transformation and tumor progression. Given the crucial role of metabolic pathway alterations in the pathogenesis of CRC, we provide an overview of novel pharmacological strategies for the treatment of CRC by targeting metabolic and signaling pathways.

Pharmacological Targeting of Epithelial-to-Mesenchymal Transition in Colorectal Cancer.

BACKGROUND: Colorectal cancer (CRC) is the third most common cause of cancer deaths, and metastasis is a major cause of mortalities. The survival rate of patients diagnosed with metastasis remains disappointing. Therefore, the prevention of tumor dissemination as well as treatment of existing metastatic lesions is an important focus of new cancer therapies. Epithelial-to-mesenchymal transition (EMT) is defined as a cellular transition from an epithelial to a mesenchymal state and determines lethal cancer characteristics consisting of invasiveness, metastasis formation, and drug resistance. METHODS: We reviewed PubMed and EMBASE libraries to collect data about pharmacological targeting of Epithelial- to-Mesenchymal Transition in colorectal cancer to prevent metastatic tumor distribution and improve the survival of patients with CRC. RESULTS: We provided an overview of the available EMT-based therapies in CRC, summarized FDA-approved and under-clinical trial drugs with EMT-inhibiting properties in metastatic CRC, and described several agents preventing EMT-associated progression and metastasis in preclinical studies. Although various preclinical and clinical findings have proven that inhibiting EMT via different pharmacological approaches can reduce aggressive features of many cancers, not all agents possessing EMT-inhibiting function in preclinical research exhibit improvement in clinical studies. CONCLUSION: Combating EMT as a therapeutic intervention with the aim of preventing tumor dissemination, eliminating exiting metastasis, and promoting resistance to therapy may be a novel and effective strategy in the treatment of CRC. We hope that further exploration of EMT-related mechanisms and EMT-inhibiting drugs will provide more opportunities to treat CRC efficiently.

The Therapeutic Potential of Targeting the Toll-like Receptor Pathway in Breast Cancer.

The toll-like receptor (TLR) signaling pathway plays a key role in inducing immune responses and is shown to be expressed in immune cells and tumor cells and is involved in the progression of several malignancies, including breast cancer. These findings provide proof of the concept of targeting this pathway as a potential therapeutic option in the treatment of breast cancer. Moreover, there is a growing body of data showing the activation of TLRs in the tumor microenvironment and its dual function as anti-tumoral (dendritic T and natural killer cells activation) or pro-tumoral activity (cell proliferation, and drug resistance). Several agents have been developed for targeting this pathway, and one of these inhibitors, called Bacillus Calmette-Guerin (an agonist of TLR2 and TLR4), is recently being approved by FDA for immunotherapy of bladder cancer. This review summarizes the current knowledge of the mechanisms of action of TLR pathways in the development/progression of cancer for better management of this disease.

Role of gut bacterial and non-bacterial microbiota in alcohol-associated liver disease: Molecular mechanisms, biomarkers, and therapeutic prospective.

Alcohol-associated liver disease (ALD) comprises a spectrum of liver diseases that include: steatosis to alcohol-associated hepatitis, cirrhosis, and ultimately hepatocellular carcinoma. The pathophysiology and potential underlying mechanisms for alcohol-associated liver disease are unclear. Moreover, the treatment of ALD remains a challenge. Intestinal microbiota include bacteria, fungi, and viruses, that are now known to be important in the development of ALD. Alcohol consumption can change the gut microbiota and function leading to liver disease. Given the importance of interactions between intestinal microbiota, alcohol, and liver injury, the gut microbiota has emerged as a potential biomarker and therapeutic target. This review focuses on the potential mechanisms by which the gut microbiota may be involved in the pathogenesis of ALD and explains how this can be translated into clinical management. We discuss the potential of utilizing the gut microbiota signature as a biomarker in ALD patients. Additionally, we present an overview of the prospect of modulating the intestinal microbiota for the management of ALD.

Angiotensin-converting Enzyme Inhibitors and Angiotensin Receptor Blockers as Potential Therapeutic Options for Pancreatic Cancer.

The renin-angiotensin system (RAS) has been reported to have a role in carcinogenesis, and therefore it may be of value as a potential therapeutic target in inhibiting tumor growth. It has been shown that inhibition of RAS via angiotensin I-converting enzyme inhibitors (ACEIs) and angiotensin II type-1 receptor (ARBs) inhibitors may have a protective effect against several malignancies. Here, we provide an overview of the potential value of the RAS pathway and targeting via ACE/ARB inhibitors in pancreatic cancer. Whilst the potential role of RAS as a target for the treatment of pancreatic cancer has been reported, the use of candesartan with gemcitabine failed to improve outcomes in pancreatic cancer. Another study of 1-3 years using ARB was found to reduce the risk of pancreatic cancer. In line with these trials, others have demonstrated that the ARBs in combination with gemcitabine might improve clinical outcomes in patients with advanced pancreatic cancer. Prospective trials are warranted to investigate this hypothesis.

The Therapeutic Potential of Targeting Key Signaling Pathways as a Novel Approach to Ameliorating Post-Surgical Adhesions.

BACKGROUND: Peritoneal adhesions (PA) are a common complication of abdominal operations. A growing body of evidence shows that inhibition of inflammation and fibrosis at sites of peritoneal damaging could prevent the development of intra-abdominal adhesions. METHODS: A search of PubMed, Medline, CINAHL and Embase databases was performed using the keywords 'postsurgical adhesion', 'post-operative adhesion', 'peritoneal adhesion', 'surgery-induced adhesion' and 'abdominal adhesion'. Studies detailing the use of pharmacological and non-pharmacological agents for peritoneal adhesion prevention were identified, and their bibliographies were thoroughly reviewed to identify further related articles. RESULTS: Several signaling pathways, such as tumor necrosis factor-alpha, tissue plasminogen activator, and type 1 plasminogen activator inhibitor, macrophages, fibroblasts, and mesothelial cells play a key part in the development of plasminogen activator. Several therapeutic approaches based on anti-PA drug barriers and traditional herbal medicines have been developed to prevent and treat adhesion formation. In recent years, the most promising method to prevent PA is treatment using biomaterial-based barriers. CONCLUSION: In this review, we provide an overview of the pathophysiology of adhesion formation and various agents targeting different pathways, including chemical agents, herbal agents, physical barriers, and clinical trials concerning this matter.

Modifications of Ribonucleases in Order to Enhance Cytotoxicity in Anticancer Therapy.

Ribonucleases (RNases) are a superfamily of enzymes that have been extensively studied since the 1960s. For a long time, this group of secretory enzymes was studied as an important model for protein chemistry such as folding, stability, and enzymatic catalysis. Since it was discovered that RNases displayed cytotoxic activity against several types of malignant cells, recent investigation has focused mainly on the biological functions and medical applications of engineered RNases. In this review, we describe the structures, functions, and mechanisms of antitumor activity of RNases. They operate at the crossroads of transcription and translation, preferentially degrading tRNA. As a result, this inhibits protein synthesis, induces apoptosis, and causes the death of cancer cells. This effect can be enhanced thousands of times when RNases are conjugated with monoclonal antibodies. Such combinations, called immunoRNases, have demonstrated selective antitumor activity against cancer cells both in vitro and in animal models. This review summarizes the current status of engineered RNases and immunoRNases as promising novel therapeutic agents for different types of cancer. Also, we describe our experimental results from published or previously unpublished research and compare them with other scientific information.