Structure and computation-guided yeast surface display for the evolution of TIMP-based matrix metalloproteinase inhibitors

The study of protein-protein interactions (PPIs) and the engineering of protein-based inhibitors often employ two distinct strategies. One approach leverages the power of combinatorial libraries, displaying large ensembles of mutant proteins, for example, on the yeast cell surface, to select binders. Another approach harnesses computational modeling, sifting through an astronomically large number of protein sequences and attempting to predict the impact of mutations on PPI binding energy. Individually, each approach has inherent limitations, but when combined, they generate superior outcomes across diverse protein engineering endeavors. This synergistic integration of approaches aids in identifying novel binders and inhibitors, fine-tuning specificity and affinity for known binding partners, and detailed mapping of binding epitopes. It can also provide insight into the specificity profiles of varied PPIs. Here, we outline strategies for directing the evolution of tissue inhibitors of metalloproteinases (TIMPs), which act as natural inhibitors of matrix metalloproteinases (MMPs). We highlight examples wherein design of combinatorial TIMP libraries using structural and computational insights and screening these libraries of variants using yeast surface display (YSD), has successfully optimized for MMP binding and selectivity, and conferred insight into the PPIs involved

The clinical application of Circulating tumor cells and DNAs as prognostic and predictive biomarkers in gastrointestinal cancer

Gastrointestinal (GI) cancer is one of the most common cancers globally. Genetic and epigenetic mechanisms are involved in its pathogenesis. The conventional methods for diagnosis and screening for GI cancers are often invasive and have other limitations. In the era of personalized medicine, a novel non-invasive approach called liquid biopsy has been introduced for the detection and management of GI cancers, which focuses on the analysis of circulating tumor cells (CTCs) and circulating cell-free tumor DNA (ctDNA). Several studies have shown that this new approach allows for an improved understanding of GI tumor biology and will lead to an improvement in clinical management. The aim of the current review is to explore the clinical applications of CTCs and ctDNA in patients with GI cancer. Key words: Gastrointestinal cancer; Liquid biopsy; CTCs; ctDNA; biomarkers; tumor biolog

Personalized medicine and new therapeutic approach in the treatment of pancreatic cancer

Pancreatic cancer (PC) is the seventh most common cause of death with a poor prognosis. Although there are many advanced therapeutic approaches, the 5-year survival rate of PC is approximately 11%, so it is one of the most aggressive cancers. The absence of potential biomarkers for early detection and screening is the main reason for poor prognosis and chemoresistance. Personalized medicine (PM) is an emerging approach using the characteristics and differences of patients in the molecular, physiological, environmental, and behavioral fields for better decision-making. In addition, novel quantitative imaging methods, including radiomic and deep learning, make a new noninvasive PM for a better early diagnosis and treatment of PC. However, PM encounters challenges that should be addressed that slow down its worldwide development, including ethical issues and relatively high costs. Here we summarize the novel therapeutic approaches and emerging research models, including patient derived xenograft (PDX) and 3-dimensional organoids, based on the patient’s tumor profile, which provides a better understanding of tumor and TME behavior and its response to drugs.</p

Bioinformatics analysis and machine learning approach applied to the identification of novel key genes involved in non-alcoholic fatty liver disease

Abstract Non-alcoholic fatty liver disease (NAFLD) comprises a range of chronic liver diseases that result from the accumulation of excess triglycerides in the liver, and which, in its early phases, is categorized NAFLD, or hepato-steatosis with pure fatty liver. The mortality rate of non-alcoholic steatohepatitis (NASH) is more than NAFLD; therefore, diagnosing the disease in its early stages may decrease liver damage and increase the survival rate. In the current study, we screened the gene expression data of NAFLD patients and control samples from the public dataset GEO to detect DEGs. Then, the correlation betweenbetween the top selected DEGs and clinical data was evaluated. In the present study, two GEO datasets (GSE48452, GSE126848) were downloaded. The dysregulated expressed genes (DEGs) were identified by machine learning methods (Penalize regression models). Then, the shared DEGs between the two training datasets were validated using validation datasets. ROC-curve analysis was used to identify diagnostic markers. R software analyzed the interactions between DEGs, clinical data, and fatty liver. Ten novel genes, including ABCF1, SART3, APC5, NONO, KAT7, ZPR1, RABGAP1, SLC7A8, SPAG9, and KAT6A were found to have a differential expression between NAFLD and healthy individuals. Based on validation results and ROC analysis, NR4A2 and IGFBP1b were identified as diagnostic markers. These key genes may be predictive markers for the development of fatty liver. It is recommended that these key genes are assessed further as possible predictive markers during the development of fatty liver

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

Abstract 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

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

Dual targeting of TGF-β and PD-L1 inhibits tumor growth in TGF-β/PD-L1-driven colorectal carcinoma

Immunosuppressive factors within the tumor microenvironment (TME), such as Transforming growth factor beta (TGF-β), constitute a crucial hindrance to immunotherapeutic approaches in colorectal cancer (CRC). Furthermore, immune checkpoint factors (e.g., programmed death-ligand 1 [PD-L1]) inhibit T-cell proliferation and activation. To cope with the inhibitory effect of immune checkpoints, the therapeutic value of dual targeting PD-L1 and TGF-β pathways via M7824 plus 5-FU in CRC has been evaluated. Integrative-systems biology approaches and RNAseq were used to assess the differential level of genes associated with 88 metastatic-CRC patients. The level of PD-L1 and TGF-β was evaluated in a validation cohort. The anti-proliferative, migratory, and apoptotic effects of PD-L1/TGF-β inhibitor, M7824, were assessed by MTT, wound-healing assay, and flow cytometry. Anti-tumor activity was assessed in a xenograft model, followed by biochemical studies and histological staining, and gene/protein expression analyses by RT-PCR and ELISA/IHC. The result of differentially expressed genes (DEGs) analysis showed 1268 upregulated and 1074 downregulated genes in CRC patients. Among the highest scoring genes and dysregulated pathways associated with CRC, PD-L1, and TGF-β were identified and further validated in 92 CRC patients. Targeting of PD-L1-TGF-β inhibited cell growth and migration, associated with modulation of CyclinD1 and MMP9. Furthermore, M7824 inhibited tumor growth via targeting TGF-β and PD-L1 pathways, resulting in modulation of inflammatory response and fibrosis via TNF-α/IL6/CD4–8 and COL1A1/1A2, respectively. In conclusion, our data illustrated that co-targeting PD-L1 and TGF-β pathways increased the effect of Fluorouracil (5-FU) and reduced the tumor growth in PD-L1/TGF-β expressing tumors, providing a new therapeutic option in the treatment of CRC.</p

Association of a genetic variant in the adenosine triphosphate transmembrane glycoprotein and risk of pancreatic cancer

Background: Pancreatic cancer (PC) is one of the most aggressive neoplasms with a poor prognosis. The association of multidrug resistance genes, MDR1/ABCB1, with the poor outcomes of several malignancies has been reported, which can be explained at least in part by a reduction in the transportation of drugs and their metabolites. Here, we explore the association of a genetic variant, rs2032582, in the ABCB1 gene with the risk of developing PC.Methods: Seventy-five patients and 188 controls were recruited. DNA was extracted followed by genotyping using Taqman® real-time polymerase chain reaction-based method. Kaplan-Meier and Cox models analysis showed that there is no significant association between genetic models and overall survival (OS) (P=0.32).Results: The frequencies of AA, AC, and CC genotypes of the variant were 29.7%, 42.2%, and 28.1%, respectively in the PC group, while the frequencies of the genotypes were 32.4%, 54.8%, and 12.8%, respectively, in the control group. Individuals with the AA genotype had an increased risk of developing PC {e.g., dominant genetic model [CC versus AA + AC: OS ± standard deviation (SD): 28±5.8 versus 50.8±6.7 months] with odds ratio (OR) of 2.67 (CI =1.33–5.34, P=0.005)}.Conclusions: Our findings demonstrated the association of the ABCB1 variant with an increased risk of PC. Further studies in a larger sample size and multi-center setting are suggested to explore the prognostic value of emerging marker PC.</p

Association of the 1236C >T variant of the multidrug resistance 1 gene with esophageal squamous cell carcinoma development and prognosis

Background: Esophageal squamous cell carcinoma (ESCC) is a common cause of cancer related mortality. There is a growing body of evidence showing an association between genetic variants in the 7q21.12 gene locus and an increased risk of cancer. Here we have explored the association between the 1236C >T; rs1128503 genetic variant of the ABCB1/MDR1 gene with the development and prognosis of ESCC. Method: Data from the computer-based patient records (CPRs) of 10 years (between 2005-2014) of the Mashhad University of Medical Sciences were used to identify patients with ESCC. Ninety-three ESCC patients and two hundred and twenty-four healthy subjects were recruited. DNA was extracted and genotyped using a Taq-Man based real-time PCR method. The association of the variant with overall-survival (OS) and progression-free survival (PFS) was analyzed using Kaplan–Meier curves, log-rank tests, and Cox model. Results: Patients with ESCC had a higher frequency of the C allele compared to the control group with Minor allele frequency (MAF) of 1.5. Moreover, 22%, and 56% of cases had TT and TC genotypes, compared to 22% for CC. Patients with TT genotype, versus CC had a higher risk of ESCC (OR: 2.3; 95%CI: 1.08-4.8, P= 0.03), although it was not associated with PFS and OS (e.g., OS of TT genotype: 56.8±6.9 months versus OS of CC genotype: 60.5±5.7 59 months). Conclusion Our data showed a relationship between a genetic polymorphism of the ABCB1 gene with clinical outcomes of the ESCC-patients, supporting further studies in a larger, and multi-centre settings followed by functional assays to assess the prognostic value of this emerging marker in esophageal squamous cell carcinoma patients