The cross-talk between macrophages and tumor cells as a target for cancer treatment.

Macrophages represent an important component of the innate immune system. Under physiological conditions, macrophages, which are essential phagocytes, maintain a proinflammatory response and repair damaged tissue. However, these processes are often impaired upon tumorigenesis, in which tumor-associated macrophages (TAMs) protect and support the growth, proliferation, and invasion of tumor cells and promote suppression of antitumor immunity. TAM abundance is closely associated with poor outcome of cancer, with impediment of chemotherapy effectiveness and ultimately a dismal therapy response and inferior overall survival. Thus, cross-talk between cancer cells and TAMs is an important target for immune checkpoint therapies and metabolic interventions, spurring interest in it as a therapeutic vulnerability for both hematological cancers and solid tumors. Furthermore, targeting of this cross-talk has emerged as a promising strategy for cancer treatment with the antibody against CD47 protein, a critical macrophage checkpoint recognized as the "don't eat me" signal, as well as other metabolism-focused strategies. Therapies targeting CD47 constitute an important milestone in the advancement of anticancer research and have had promising effects on not only phagocytosis activation but also innate and adaptive immune system activation, effectively counteracting tumor cells' evasion of therapy as shown in the context of myeloid cancers. Targeting of CD47 signaling is only one of several possibilities to reverse the immunosuppressive and tumor-protective tumor environment with the aim of enhancing the antitumor response. Several preclinical studies identified signaling pathways that regulate the recruitment, polarization, or metabolism of TAMs. In this review, we summarize the current understanding of the role of macrophages in cancer progression and the mechanisms by which they communicate with tumor cells. Additionally, we dissect various therapeutic strategies developed to target macrophage-tumor cell cross-talk, including modulation of macrophage polarization, blockade of signaling pathways, and disruption of physical interactions between leukemia cells and macrophages. Finally, we highlight the challenges associated with tumor hypoxia and acidosis as barriers to effective cancer therapy and discuss opportunities for future research in this field.

Genomic Analysis, Evolution and Characterization of E3 Ubiquitin Protein Ligase (TRIM) Gene Family in Common Carp (Cyprinus carpio).

Tripartite motifs (TRIM) is a large family of E3 ubiquitin ligases that play an important role in ubiquitylation. TRIM proteins regulate a wide range of biological processes from cellular response to viral infection and are implicated in various pathologies, from Mendelian disease to cancer. Although the TRIM family has been identified and characterized in tetrapods, but the knowledge about common carp and other teleost species is limited. The genes and proteins in the TRIM family of common carp were analyzed for evolutionary relationships, characterization, and functional annotation. Phylogenetic analysis was used to elucidate the evolutionary relationship of TRIM protein among teleost and higher vertebrate species. The results show that the TRIM orthologs of highly distant vertebrates have conserved sequences and domain architectures. The pairwise distance was calculated among teleost species of TRIMs, and the result exhibits very few mismatches at aligned position thus, indicating that the members are not distant from each other. Furthermore, TRIM family of common carp clustered into six groups on the basis of phylogenetic analysis. Additionally, the analysis revealed conserved motifs and functional domains in the subfamily members. The difference in functional domains and motifs is attributed to the evolution of these groups from different ancestors, thus validating the accuracy of clusters in the phylogenetic tree. However, the intron-exon organization is not precisely similar, which suggests duplication of genes and complex alternative splicing. The percentage of secondary structural elements is comparable for members of the same group, but the tertiary conformation is varied and dominated by coiled-coil segments required for catalytic activity. Gene ontology analysis revealed that these proteins are mainly associated with the catalytic activity of ubiquitination, immune system, zinc ion binding, positive regulation of transcription, ligase activity, and cell cycle regulation. Moreover, the biological pathway analyses identified four KEGG and 22 Reactome pathways. The predicted pathways correspond to functional domains, and gene ontology which proposes that proteins with similar structures might perform the same functions.

System level modeling and analysis of TNF-α mediated sphingolipid signaling pathway in neurological disorders for the prediction of therapeutic targets.

Sphingomyelin (SM) belongs to a class of lipids termed sphingolipids. The disruption in the sphingomyelin signaling pathway is associated with various neurodegenerative disorders. TNF-α, a potent pro-inflammatory cytokine generated in response to various neurological disorders like Alzheimer's disease (AD), Parkinson's disease (PD), and Multiple Sclerosis (MS), is an eminent regulator of the sphingomyelin metabolic pathway. The immune-triggered regulation of the sphingomyelin metabolic pathway via TNF-α constitutes the sphingomyelin signaling pathway. In this pathway, sphingomyelin and its downstream sphingolipids activate various signaling cascades like PI3K/AKT and MAPK/ERK pathways, thus, controlling diverse processes coupled with neuronal viability, survival, and death. The holistic analysis of the immune-triggered sphingomyelin signaling pathway is imperative to make necessary predictions about its pivotal components and for the formulation of disease-related therapeutics. The current work offers a comprehensive in silico systems analysis of TNF-α mediated sphingomyelin and downstream signaling cascades via a model-based quantitative approach. We incorporated the intensity values of genes from the microarray data of control individuals from the AD study in the input entities of the pathway model. Computational modeling and simulation of the inflammatory pathway enabled the comprehensive study of the system dynamics. Network and sensitivity analysis of the model unveiled essential interaction parameters and entities during neuroinflammation. Scanning of the key entities and parameters allowed us to determine their ultimate impact on neuronal apoptosis and survival. Moreover, the efficacy and potency of the FDA-approved drugs, namely Etanercept, Nivocasan, and Scyphostatin allowed us to study the model's response towards inhibition of the respective proteins/enzymes. The network analysis revealed the pivotal model entities with high betweenness and closeness centrality values including recruit FADD, TNFR_TRADD, act CASP2, actCASP8, actCASP3 and 9, cytochrome C, and RIP_RAIDD which profoundly impacted the neuronal apoptosis. Whereas some of the entities with high betweenness and closeness centrality values like Gi-coupled receptor, actS1PR, Sphingosine, S1P, actAKT, and actERK produced a high influence on neuronal survival. However, the current study inferred the dual role of ceramide, both on neuronal survival and apoptosis. Moreover, the drug Nivocasan effectively reduces neuronal apoptosis via its inhibitory mechanism on the caspases.

An Extensive Review on Preclinical and Clinical Trials of Oncolytic Viruses Therapy for Pancreatic Cancer.

Chemotherapy resistance and peculiar tumor microenvironment, which diminish or mitigate the effects of therapies, make pancreatic cancer one of the deadliest malignancies to manage and treat. Advanced immunotherapies are under consideration intending to ameliorate the overall patient survival rate in pancreatic cancer. Oncolytic viruses therapy is a new type of immunotherapy in which a virus after infecting and lysis the cancer cell induces/activates patients' immune response by releasing tumor antigen in the blood. The current review covers the pathways and molecular ablation that take place in pancreatic cancer cells. It also unfolds the extensive preclinical and clinical trial studies of oncolytic viruses performed and/or undergoing to design an efficacious therapy against pancreatic cancer.

Interaction Analysis of Adenovirus L5 Protein With Pancreatic Cancer Cell Surface Receptor to Analyze Its Affinity for Oncolytic Virus Therapy.

This study seeks to investigate the interaction profile of the L5 protein of oncolytic adenovirus with the overexpressed surface receptors of pancreatic cancer. This is an important area of research because pancreatic cancer is one of the most fatal malignancies with a very low patient survival rate. Multiple therapies to date to improve the survival rate are reported; however, they show a comparatively low success rate. Among them, oncolytic virus therapy is a type of immunotherapy that is currently under deliberation by researchers for multiple cancer types in various clinical trials. Talimogene laherparepvec (T-VEC) is the first oncolytic virus approved by the US Food and Drug Administration (FDA) for melanoma. The oncolytic virus not only kills cancer cells but also activates the anticancer immune response. Therefore, it is preferred over others to deal with aggressive pancreatic cancer. The efficacy of therapy primarily depends on how effectively the oncolytic virus enters and infects the cancer cell. Cell surface receptors and their interactions with virus coat proteins are a crucial step for oncolytic virus entry and a pivotal determinant. The L5 proteins of the virus coat are the first to interact with host cell surface receptors. Therefore, the objective of this study is to analyze the interaction profile of the L5 protein of oncolytic adenovirus with overexpressed surface receptors of pancreatic cancer. The L5 proteins of three adenovirus serotypes HAdV2, HAdV5, and HAdV3 were utilized in this study. Overexpressed pancreatic cancer receptors include SLC2A1, MET, IL1RAP, NPR3, GABRP, SLC6A6, and TMPRSS4. The protein structures of viral and cancer cell protein were docked using the High Ambiguity Driven protein-protein DOCKing (HADDOCK) server. The binding affinity and interaction profile of viral proteins against all the receptors were analyzed. Results suggest that the HAdV3 L5 protein shows better interaction as compared to HAdV2 and HAdV5 by elucidating high binding affinity with 4 receptors (NPR3, GABRP, SLC6A6, and TMPRSS4). The current study proposed that HAdV5 or HAdV2 virus pseudotyped with the L5 protein of HAdV3 can be able to effectively infect pancreatic cancer cells. Moreover, the current study surmises that the affinity maturation of HAdV3 L5 can enhance virus attachment with all the receptors of cancer cells.

Integrated Analysis of Microarray and RNA-Seq Data for the Identification of Hub Genes and Networks Involved in the Pancreatic Cancer.

Pancreatic cancer (PaCa) is the seventh most fatal malignancy, with more than 90% mortality rate within the first year of diagnosis. Its treatment can be improved the identification of specific therapeutic targets and their relevant pathways. Therefore, the objective of this study is to identify cancer specific biomarkers, therapeutic targets, and their associated pathways involved in the PaCa progression. RNA-seq and microarray datasets were obtained from public repositories such as the European Bioinformatics Institute (EBI) and Gene Expression Omnibus (GEO) databases. Differential gene expression (DE) analysis of data was performed to identify significant differentially expressed genes (DEGs) in PaCa cells in comparison to the normal cells. Gene co-expression network analysis was performed to identify the modules co-expressed genes, which are strongly associated with PaCa and as well as the identification of hub genes in the modules. The key underlaying pathways were obtained from the enrichment analysis of hub genes and studied in the context of PaCa progression. The significant pathways, hub genes, and their expression profile were validated against The Cancer Genome Atlas (TCGA) data, and key biomarkers and therapeutic targets with hub genes were determined. Important hub genes identified included ITGA1, ITGA2, ITGB1, ITGB3, MET, LAMB1, VEGFA, PTK2, and TGFß1. Enrichment analysis characterizes the involvement of hub genes in multiple pathways. Important ones that are determined are ECM-receptor interaction and focal adhesion pathways. The interaction of overexpressed surface proteins of these pathways with extracellular molecules initiates multiple signaling cascades including stress fiber and lamellipodia formation, PI3K-Akt, MAPK, JAK/STAT, and Wnt signaling pathways. Identified biomarkers may have a strong influence on the PaCa early stage development and progression. Further, analysis of these pathways and hub genes can help in the identification of putative therapeutic targets and development of effective therapies for PaCa.

A Computational Systems Analyses to Identify Biomarkers and Mechanistic Link in Psoriasis and Cutaneous Squamous Cell Carcinoma.

Psoriasis is the most common and chronic skin disease that affects individuals from every age group. The rate of psoriasis is increasing over the time in both developed and developing countries. Studies have revealed the possibility of association of psoriasis with skin cancers, particularly non-melanoma skin cancers (NMSC), which, include basal cell carcinoma and cutaneous squamous cell carcinoma (cSCC). There is a need to analyze the disease at molecular level to propose potential biomarkers and therapeutic targets in comparison to cSCC. Therefore, the second analyzed disease of this study is cSCC. It is the second most common prevalent skin cancer all over the world with the potential to metastasize and recur. There is an urge to validate the proposed biomarkers and discover new potential biomarkers as well. In order to achieve the goals and objectives of the study, microarray and RNA-sequencing data analyses were performed followed by network analysis. Afterwards, quantitative systems biology was implemented to analyze the results at a holistic level. The aim was to predict the molecular patterns that can lead psoriasis to cancer. The current study proposed potential biomarkers and therapeutic targets for psoriasis and cSCC. IL-17 signaling pathway is also identified as significant pathway in both diseases. Moreover, the current study proposed that autoimmune pathology, neutrophil recruitment, and immunity to extracellular pathogens are sensitive towards MAPKs (MAPK13 and MAPK14) and genes for AP-1 (FOSL1 and FOS). Therefore, these genes should be further studied in gene knock down based studies as they may play significant role in leading psoriasis towards cancer.

Long non-coding RNAs and their targets as potential biomarkers in breast cancer.

Breast cancer is among the lethal types of cancer with a high mortality rate, globally. Its high prevalence can be controlled through improved analysis and identification of disease-specific biomarkers. Recently, long non-coding RNAs (lncRNAs) have been reported as key contributors of carcinogenesis and regulate various cellular pathways through post-transcriptional regulatory mechanisms. The specific aim of this study was to identify the novel interactions of aberrantly expressed genetic components in breast cancer by applying integrative analysis of publicly available expression profiles of both lncRNAs and mRNAs. Differential expression patterns were identified by comparing the breast cancer expression profiles of samples with controls. Significant co-expression networks were identified through WGCNA analysis. WGCNA is a systems biology approach used to elucidate the pattern of correlation between genes across microarray samples. It is also used to identify the highly correlated modules. The results obtained from this study revealed significantly differentially expressed and co-expressed lncRNAs and their cis- and trans-regulating mRNA targets which include RP11-108F13.2 targeting TAF5L, RPL23AP2 targeting CYP4F3, CYP4F8 and AL022324.2 targeting LRP5L, AL022324.3, and Z99916.3, respectively. Moreover, pathway analysis revealed the involvement of identified mRNAs and lncRNAs in major cell signalling pathways, and target mRNAs expression is also validated through cohort data. Thus, the identified lncRNAs and their target mRNAs represent novel biomarkers that could serve as potential therapeutics for breast cancer and their roles could also be further validated through wet labs to employ them as potential therapeutic targets in future.

MicroRNAs and their target mRNAs as potential biomarkers among smokers and non-smokers with lung adenocarcinoma.

Lung adenocarcinoma is one of the major causes of mortality. Current methods of diagnosis can be improved through identification of disease specific biomarkers. MicroRNAs are small non-coding regulators of gene expression, which can be potential biomarkers in various diseases. Thus, the main objective of this study was to gain mechanistic insights into genetic abnormalities occurring in lung adenocarcinoma by implementing an integrative analysis of miRNAs and mRNAs expression profiles in the case of both smokers and non-smokers. Differential expression was analysed by comparing publicly available lung adenocarcinoma samples with controls. Furthermore, weighted gene co-expression network analysis is performed which revealed mRNAs and miRNAs significantly correlated with lung adenocarcinoma. Moreover, an integrative analysis resulted in identification of several miRNA-mRNA pairs which were significantly dysregulated in non-smokers with lung adenocarcinoma. Also two pairs (miR-133b/Protein Kinase C Zeta (PRKCZ) and miR-557/STEAP3) were found specifically dysregulated in smokers. Pathway analysis further revealed their role in important signalling pathways including cell cycle. This analysis has not only increased the authors' understanding about lung adenocarcinoma but also proposed potential biomarkers. However, further wet laboratory studies are required for the validation of these potential biomarkers which can be used to diagnose lung adenocarcinoma.

MCaVoH: A toolkit for mining, classification and visualization of human microbiota.

Human body is the home for a large number of microbes. The complexity of enterotype depends on the body site. Microbial communities in various samples from different regions are being classified on the basis of 16S rRNA gene sequences. With the improvement in sequencing technologies various computational methods have been used for the analysis of microbiome data. Despite several available machine learning techniques there is no single platform available which could provide several techniques for clustering, multiclass classification, comparative analysis and the most significantly the identification of the subgroups present within larger groups of human microbial communities. We present a tool named MCaVoH for this purpose which performs clustering and classification of 16S rRNA sequence data and highlight various groups. Our tool has an added facility of biclustering which produces local group of communities present within larger groups (clusters). The core objective of our work was to identify the interaction between various bacterial species along with monitoring the composition and variations in microbial communities. MCaVoH also evaluates the performance and efficiency of different techniques using comparative analysis. The results are visualized through different plots and graphs. We implemented our tool in MATLAB. We tested our tool on several real and simulated 16S rRNA data sets and it outperforms several existing methods. Our tool provides a single platform for using multiple clustering, classification algorithms, local community identification along with their comparison which has not been done so far. Tool is available at https://sourceforge.net/projects/mcavoh/.