High expression of bone morphogenetic protein 1 (BMP1) is associated with a poor survival rate in human gastric cancer, a dataset approaches

PCPE2: Expression of Multifunctional Extracellular Glycoprotein Associates With Diverse Cellular Functions

Procollagen C-endopeptidase enhancer 2, known as PCPE2 or PCOC2 (gene name, PCOLCE2) is a glycoprotein that resides in the extracellular matrix, and is similar in domain organization to PCPE1 / PCPE, PCOC1 (PCOLCE1 /PCOLCE). Due to the many similarities between the two related proteins, PCPE2 has been assumed to have biological functions similar to PCPE. PCPE is a well-established enhancer of procollagen processing activating the enzyme, BMP-1. However, reports show that PCPE2 has a strikingly different tissue expression profile compared to PCPE. With that in mind and given the paucity of published studies on PCPE2, this review examines the current literature citing PCPE2 and its association with specific cell types and signaling pathways. Additionally, this review will present a brief history of PCPE2's discovery, highlighting structural and functional similarities and differences compared to PCPE. Considering the widespread use of RNA sequencing techniques to examine associations between cell-specific gene expression and disease states, we will show that PCPE2 is repeatedly found as a differentially regulated gene (DEG) significantly associated with a number of cellular processes, well beyond the scope of procollagen fibril processing.

Matrisomics: Beyond the extracellular matrix for unveiling tumor microenvironment

The matrisome, a group of proteins constituting or interacting with the extracellular matrix (ECM), has garnered attention as a potent regulator of cancer progression. An increasing number of studies have focused on cancer matrisome utilizing diverse -omics approaches. Here, we present diverse patterns of matrisomal populations within cancer tissues, exploring recent -omics studies spanning different '-omics' levels (epigenomics, genomics, transcriptomics, and proteomics), as well as newly developed sequencing techniques such as single-cell RNA sequencing and spatial transcriptomics. Some matrisome genes showed uniform patterns of upregulated or downregulated expression across various cancers, while others displayed different expression patterns according to the cancer types. This matrisomal dysregulation in cancer was further examined according to their originating cell type and spatial location in the tumor tissue. Experimental studies were also collected to demonstrate the identified roles of matrisome genes during cancer progression. Interestingly, many studies on cancer matrisome have suggested matrisome genes as effective biomarkers in cancer research. Although the specific mechanisms and clinical applications of cancer matrisome have not yet been fully elucidated, recent techniques and analyses on cancer matrisomics have emphasized their biological importance in cancer progression and their clinical implications in deciding the efficacy of cancer treatment.

Assessment of circulating proteins in thyroid cancer: Proteome-wide Mendelian randomization and colocalization analysis

The causality between circulating proteins and thyroid cancer (TC) remains unclear. We employed five large-scale circulating proteomic genome-wide association studies (GWASs) with up to 100,000 participants and a TC meta-GWAS (nCase = 3,418, nControl = 292,703) to conduct proteome-wide Mendelian randomization (MR) and Bayesian colocalization analysis. Protein and gene expressions were validated in thyroid tissue. Through MR analysis, we identified 26 circulating proteins with a putative causal relationship with TCs, among which NANS protein passed multiple corrections (P BH = 3.28e-5, 0.05/1,525). These proteins were involved in amino acids and organic acid synthesis pathways. Colocalization analysis further identified six proteins associated with TCs (VCAM1, LGMN, NPTX1, PLEKHA7, TNFAIP3, and BMP1). Tissue validation confirmed BMP1, LGMN, and PLEKHA7's differential expression between normal and TC tissues. We found limited evidence for linking circulating proteins and the risk of TCs. Our study highlighted the contribution of proteins, particularly those involved in amino acid metabolism, to TCs.

A neural-related gene risk score for head and neck squamous cell carcinoma

OBJECTIVES

This study aimed to establish a neural-related gene risk score (NRGRS) for the prediction of head and neck squamous cell carcinoma prognosis and explore its predictive value on the benefit of immune checkpoint inhibitor therapy.

METHODS

Based on the transcriptome data of HNSCC patients (n = 546) from The Cancer Genome Atlas database, 37 neural-related hub genes were identified by weighted gene co-expression network analysis. Four genes (ITGA5, PYGM, GNG7 and ATP2A3) were identified to construct NRGRS using Lasso-Cox regression method based on the derivation cohort and validated in the Gene Expression Omnibus cohort (n = 109). The survival analysis was performed to validate the prognostic value of NRGRS and immune characteristics in NRGRS-defined subgroups were analyzed.

RESULTS

NRGRS-high patients had a worse overall survival than NRGRS-low patients. Tumors with high NRGRS were more likely to have high infiltration of naive CD4+ T cells, M0, M2 macrophages and resting mast cells, which illustrated suppressive immunity and less benefit from immunotherapy therapy.

CONCLUSION

NRGRS strongly correlates with survival and is a promising biomarker to predict immunotherapy benefits for head and neck cancer patients. This study provides evidence for the potential correlation between neural-related transcriptome alteration and immune activity.

Identification of gastric cancer types based on hyperspectral imaging technology

Gastric cancer is becoming the second biggest cause of death from cancer. Treatment and prognosis of different types of gastric cancer vary greatly. However, the routine pathological examination is limited to the tissue level and is easily affected by subjective factors. In our study, we examined gastric mucosal samples from 50 normal tissue and 90 cancer tissues. Hyperspectral imaging technology was used to obtain spectral information. A two-classification model for normal tissue and cancer tissue identification and a four-classification model for cancer type identification are constructed based on the improved deep residual network (IDRN). The accuracy of the two-classification model and four-classification model are 0.947 and 0.965. Hyperspectral imaging technology was used to extract molecular information to realize real-time diagnosis and accurate typing. The results show that hyperspectral imaging technique has good effect on diagnosis and type differentiation of gastric cancer, which is expected to be used in auxiliary diagnosis and treatment.

Molecular Docking and Simulation-Binding Analysis of Plant Phytochemicals with the Hepatocellular Carcinoma Targets Epidermal Growth Factor Receptor and Caspase-9

Among primary liver cancers, hepatocellular carcinoma (HCC) is one of the most common forms and it has been categorized as the joint-fourth largest reason of cancer-related deaths globally. Different factors such as alcohol abuse, hepatitis B and C, viral infections, and fatty liver diseases are mainly related to the pathogenesis of HCC. In the current study, 1000 total various plant phytochemicals were docked to proteins involved in HCC. The compounds were docked to the active site amino acids of epidermal growth factor receptor and caspase-9 as receptor proteins in order to explore their inhibiting potential. The top five compounds against each receptor protein were explored as potential drug candidates on the basis of their binding affinity and root-mean square deviation values. The top two compounds against each protein were found to be liquoric acid (S-score -9.8 kcal/mol) and madecassic acid (S-score -9.3 kcal/mol) against EGFR, and limonin (S-score -10.5 kcal/mol) and obamegine (S-score -9.3 kcal/mol) against the caspase-9 protein. The selected phytochemicals were further assessed through drug scanning using Lipinski's rule of five to explore their molecular properties and druggability. According to the ADMET analysis, the selected phytochemicals were found to be non-toxic and non-carcinogenic. Finally, the molecular dynamics simulation study revealed that liquoric acid and limonin were stabilized within the binding pockets of EGFR and capase-9, respectively, and stayed firmly bound throughout the simulation. In light of the current findings, the phytochemicals reported in this study, especially liquoric acid and limonin, could be used as potential drugs for the treatment of HCC in the future.

Identification of risk variants related to malignant tumors in children with birth defects by whole genome sequencing

Background

Children with birth defects (BD) are more likely to develop cancer and the increased risk of cancer persists into adulthood. Prior population-based assessments have demonstrated that even non-chromosomal BDs are associated with at least two-fold increase of cancer risk. Identification of variants that are associated with malignant tumor in BD patients without chromosomal anomalies may improve our understanding of the underlying molecular mechanisms and provide clues for early cancer detection in children with BD.

Methods

In this study, whole genome sequencing (WGS) data of blood-derived DNA for 1653 individuals without chromosomal anomalies were acquired from the Kids First Data Resource Center (DRC), including 541 BD probands with at least one type of malignant tumors, 767 BD probands without malignant tumor, and 345 healthy family members who are the parents or siblings of the probands. Recurrent variants exclusively seen in cancer patients were selected and mapped to their corresponding genomic regions. The targeted genes/non-coding RNAs were further reduced using random forest and forward feature selection (ffs) models.

Results

The filtered genes/non-coding RNAs, including variants in non-coding areas, showed enrichment in cancer-related pathways. To further support the validity of these variants, blood WGS data of additional 40 independent BD probands, including 25 patients with at least one type of cancers from unrelated projects, were acquired. The counts of variants of interest identified in the Kid First data showed clear deviation in the validation dataset between BD patients with cancer and without cancer. Furthermore, a deep learning model was built to assess the predictive abilities in the 40 patients using variants of interest identified in the Kids First cohort as feature vectors. The accuracies are ~ 75%, with the noteworthy observation that variants mapped to non-coding regions provided the highest accuracy (31 out of 40 patients were labeled correctly).

Conclusion

We present for the first time a panorama of genetic variants that are associated with cancers in non-chromosomal BD patients, implying that our approach may potentially serve for the early detection of malignant tumors in patients with BD.

In silico investigation of the role of vitamins in cancer therapy through inhibition of MCM7 oncoprotein

An overabundance of MCM7 protein, a component of the minichromosome maintenance complex that normally initiates DNA replication, has been reported to cause different types of cancers with aggressive malignancy. Inhibition of MCM7 may lead to a significant reduction in cancer-associated cell proliferation. Despite such significance of MCM7 in cancer, the protein structure is yet to be resolved experimentally. This significantly halts the structure-guided ligand designing for cancer therapy targeting the MCM7. The present study aims to resolve the tertiary structure of MCM7 and repurpose the FDA-approved clinically used drugs for cancer therapy by targeting MCM7 protein. The secondary and 3D structures of MCM7 were generated using multiple bioinformatics tools, including the Self-Optimized Prediction Method with Alignment (SOPMA), SWISS-MODEL, and I-TASSER. The reliability of the modeled structure was assessed using PROCHECK. Initially, a structure-guided virtual screening was performed on the approved drug library to identify potential hits against MCM7. The detailed molecular mechanism of receptor interactions of the identified hits was evaluated using extensive molecular dynamics simulation. The results from this study reveal an intriguing discovery of the potential of ergocalciferol (vitamin D2), cholecalciferol (vitamin D3), ergosterol (precursor of vitamin D2) and menaquinone (vitamin K2) as oncoprotein inhibitors for cancer therapy via inhibition of MCM7.

An Analysis of BMP1 Associated with m6A Modification and Immune Infiltration in Pancancer

Background. This research explores the underlying link between diagnosis and therapy between bone morphogenetic protein 1 (BMP1) and various cancers. Methods. Three immunotherapeutic cohorts, by the composition of IMvigor210, GSE35640, and GSE78220 were obtained from previously published articles and the Gene Expression Omnibus database. The different expressions of BMP1 in various clinical parameters were conducted, and prognostic analysis was executed utilizing Cox proportional hazard regression and Gene Expression Profiling Interactive Analysis. Moreover, the correlation between BMP1 and tumor microenvironment was analyzed using ESTIMATE and CIBERSORT algorithms. Tumor mutational burden and microsatellite instability were also included. The correlation between m6A modification and the gene expression level was analyzed using Tumor IMmune Estimation Resource, the University of Alabama at Birmingham Cancer data analysis portal. Gene Set Cancer Analysis analyzed the correlation of BMP1 expression level with copy number variations and methylation. Furthermore, the correlation between BMP1 and therapeutic response after antineoplastic drug use was illustrated for further discussion. Results. BMP1 expression had significant differences in 14 cancers. It presented an intimate relationship with immune-relevant biomarkers. A variation analysis indicated that BMP1 had a significant association with immunotherapeutic response. The expression level of BMP1 was closely associated with insulin-like growth factor binding protein 3, an m6A modification relative gene. Except for a few cancer types, methylation negatively correlated with BMP1, and copy number variations positively correlated with BMP1. Notably, low BMP1 expression was connected with immunotherapeutic response in the cohorts, and its expression was related to increased sectional sensitivity of drugs. Conclusion. BMP1 may serve as a potential biomarker for prognostic prediction and immunologic infiltration in diversified cancers, providing a new thought approach for oncotherapy.

Targeting SARS-CoV-2 non-structural protein 13 via helicase-inhibitor-repurposing and non-structural protein 16 through pharmacophore-based screening

Novel drug compound hunting was carried out for SARS-CoV-2 proteins with low mutation susceptibility. The probability of escape mutation and drug resistance is lower if conserved microbial proteins are targeted by therapeutic drugs. Mutation rate of all SARS-CoV-2 proteins were analyzed via multiple sequence alignment Non-Structural Protein 13 and Non-Structural Protein 16 were selected for the current study due to low mutation rate among viral strains and significant functionality. Cross-species mutation rate analysis for NSP13 and NSP16 showed these are well-conserved proteins among four coronaviral species. Viral helicase inhibitors, identified using literature-mining, were docked against NSP13. Pharmacophore-based screening of 11,375 natural compounds was conducted for NSP16. Stabilities of top compounds inside human body were confirmed via molecular dynamic simulation. ADME properties and LD₅₀ values of the helicase inhibitors and Ambinter natural compounds were analyzed. Compounds against NSP13 showed binding affinities between −10 and −5.9 kcal/mol whereby ivermectin and scutellarein showed highest binding energies of −10 and −9.9 kcal/mol. Docking of 18 hit compounds against NSP16 yielded binding affinities between −8.9 and −4.1 kcal/mol. Hamamelitannin and deacyltunicamycin were the top compounds with binding affinities of −8.9 kcal/mol and −8.4 kcal/mol. The top compounds showed stable ligand–protein interactions in molecular dynamics simulation. The analyses revealed two hit compounds against each targeted protein displaying stable behavior, high binding affinity and molecular interactions. Conversion of these compounds into drugs after in vitro experimentation can become better treatment options to elevate COVID management.

A Novel Risk Score Model Based on Eleven Extracellular Matrix-Related Genes for Predicting Overall Survival of Glioma Patients

Gliomas are the most common lethal primary brain tumors with variable survival outcomes for patients. The extracellular matrix (ECM) is linked with clinical prognosis of glioma patients, but it is not commonly used as a clinical indicator. Herein, we investigated changes in ECM-related genes (ECMRGs) via analyzing the transcriptional data of 938 gliomas from TCGA and CGGA datasets. Based on least absolute shrinkage and selection operator (LASSO) Cox regression analysis, a 11-ECMRG signature that is strongly linked with overall survival (OS) in glioma patients was identified. This signature was characterized by high-risk and low-risk score patterns. We found that the patients in the high-risk group are significantly linked with malignant molecular features and worse outcomes. Univariate and multivariate Cox regression analyses suggested that the signature is an independent indicator for glioma prognosis. The prediction accuracy of the signature was verified through time-dependent receiver operating characteristic (ROC) curves and calibration plots. Further bioinformatics analyses implied that the ECMRG signature is strongly associated with the activation of multiple oncogenic and metabolic pathways and immunosuppressive tumor microenvironment in gliomas. In addition, we confirmed that the high-risk score is an indicator for a therapy-resistant phenotype. In addition to bioinformatics analyses, we functionally verified the oncogenic role of bone morphogenetic protein 1 (BMP1) in gliomas in vitro.

Toward the Identification of Natural Antiviral Drug Candidates against Merkel Cell Polyomavirus: Computational Drug Design Approaches

Merkel cell carcinoma (MCC) is a rare form of aggressive skin cancer mainly caused by Merkel cell polyomavirus (MCPyV). Most MCC tumors express MCPyV large T (LT) antigens and play an important role in the growth-promoting activities of oncoproteins. Truncated LT promotes tumorigenicity as well as host cell proliferation by activating the viral replication machinery, and inhibition of this protein in humans drastically lowers cellular growth linked to the corresponding cancer. Our study was designed with the aim of identifying small molecular-like natural antiviral candidates that are able to inhibit the proliferation of malignant tumors, especially those that are aggressive, by blocking the activity of viral LT protein. To identify potential compounds against the target protein, a computational drug design including molecular docking, ADME (absorption, distribution, metabolism, and excretion), toxicity, molecular dynamics (MD) simulation, and molecular mechanics generalized Born surface area (MM-GBSA) approaches were applied in this study. Initially, a total of 2190 phytochemicals isolated from 104 medicinal plants were screened using the molecular docking simulation method, resulting in the identification of the top five compounds having the highest binding energy, ranging between −6.5 and −7.6 kcal/mol. The effectiveness and safety of the selected compounds were evaluated based on ADME and toxicity features. A 250 ns MD simulation confirmed the stability of the selected compounds bind to the active site (AS) of the target protein. Additionally, MM-GBSA analysis was used to determine the high values of binding free energy (ΔG bind) of the compounds binding to the target protein. The five compounds identified by computational approaches, Paulownin (CID: 3084131), Actaealactone (CID: 11537736), Epigallocatechin 3-O-cinnamate (CID: 21629801), Cirsilineol (CID: 162464), and Lycoricidine (CID: 73065), can be used in therapy as lead compounds to combat MCPyV-related cancer. However, further wet laboratory investigations are required to evaluate the activity of the drugs against the virus.

Identification of three immune subtypes characterized by distinct tumor immune microenvironment and therapeutic response in stomach adenocarcinoma

BACKGROUND

In primary stomach adenocarcinoma (STAD), the tumor immune microenvironment (TIME) is important for cancer occurrence and progression; however, its clinical significance remains unclear. This study investigated the association between patient survival, TIME, and therapeutic response to STAD.

METHODS

Gene expression profiles of STAD cases were collected from the Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus. Molecular subtypes were explored with consistent clustering methods according to 119 immune signatures and the infiltrating scores of 22 immune cells using the Multi-Omics Immuno-Oncology Biological Research algorithm. We determined IFNγ scores and immune cytolytic activity (CYT) scores on the basis of corresponding gene signatures via single-sample Gene Set Enrichment Analysis. Comparisons of survival, TIME, 10 immunity-related oncogenic pathways, immune checkpoint expression, and therapeutic response were conducted among the three subtypes. We further applied linear discriminant analysis to construct a characteristic index to classify the subtypes, and the Pearson correlation coefficient for the relationship between the index and immune checkpoint genes. Weighted Correlation Network Analysis (WGCNA) was used to mine the associated modules and specific genes.

RESULTS

We collected gene expression profiles from 352 STAD cases in the TCGA database, 300 in GSE62254, and 344 in GSE84437. Three STAD subtypes (IS1-IS3) were established according to the TIME signatures. The IS3 subtype had the highest immune score and the best prognosis, as well as markedly increased immune T-cell CYT, Th1/IFNγ scores, and immune checkpoint gene expression, compared to the other two subtypes. It was highly similar to the PD-1 response group in the previous study samples of GSE91061. The established TIME classification index performed well in classifying subtypes and was directly proportional to immune checkpoint-related gene expression levels. WGCNA explored 6 modules and 14 genes, namely DYSF, MAN1C1, HTRA3, EMCN, RFLNB, KANK3, MAGEH1, CD93, PCAT19, FUT11, BMP1, FOSB, DCHS1, and TCF3, which were associated with the established TIME classification index and STAD patient prognosis.

CONCLUSION

TIME phenotypes of STAD patients could be divided into three different molecular subtypes, which displayed different prognoses, immune features, and therapeutic responses. Our results shed new light on predicting patient outcomes and the discovery of new anti-STAD therapeutic strategies according to the TIME.

ELK3 Controls Gastric Cancer Cell Migration and Invasion by Regulating ECM Remodeling-Related Genes

Current therapeutic strategies for gastric cancer, including surgery and chemotherapy improve patient survival; however, the survival rate of patients with metastatic gastric cancer is very low. The molecular mechanisms underlying the dissemination of gastric cancer cells to distant organs are currently unknown. Here, we demonstrate that the E26 transformation-specific (ETS) transcription factor ELK3 (ELK3) gene is required for the migration and invasion of gastric cancer cells. The ELK3 gene modulates the expression of extracellular matrix (ECM) remodeling-related genes, such as bone morphogenetic protein (BMP1), lysyl oxidase like 2 (LOXL2), Snail family transcriptional repressor 1 (SNAI1), serpin family F member 1 (SERPINF1), decorin (DCN), and nidogen 1 (NID1) to facilitate cancer cell dissemination. Our in silico analyses indicated that ELK3 expression was positively associated with these ECM remodeling-related genes in gastric cancer cells and patient samples. The high expressions of ELK3 and other ECM remodeling-related genes were also closely associated with a worse prognosis of patients with gastric cancer. Collectively, these findings suggest that ELK3 acts as an important regulator of gastric cancer cell dissemination by regulating ECM remodeling.

A tumour-resident Lgr5+ stem-cell-like pool drives the establishment and progression of advanced gastric cancers

Gastric cancer is among the most prevalent and deadliest of cancers globally. To derive mechanistic insight into the pathways governing this disease, we generated a Claudin18-IRES-CreERT2 allele to selectively drive conditional dysregulation of the Wnt, Receptor Tyrosine Kinase and Trp53 pathways within the gastric epithelium. This resulted in highly reproducible metastatic, chromosomal-instable-type gastric cancer. In parallel, we developed orthotopic cancer organoid transplantation models to evaluate tumour-resident Lgr5⁺ populations as functional cancer stem cells via in vivo ablation. We show that Cldn18 tumours accurately recapitulate advanced human gastric cancer in terms of disease morphology, aberrant gene expression, molecular markers and sites of distant metastases. Importantly, we establish that tumour-resident Lgr5⁺ stem-like cells are critical to the initiation and maintenance of tumour burden and are obligatory for the establishment of metastases. These models will be invaluable for deriving clinically relevant mechanistic insights into cancer progression and as preclinical models for evaluating therapeutic targets.

Spike protein recognizer receptor ACE2 targeted identification of potential natural antiviral drug candidates against SARS-CoV-2

Angiotensin-converting enzyme 2 (ACE2), also known as peptidyl-dipeptidase A, belongs to the dipeptidyl carboxydipeptidases family has emerged as a potential antiviral drug target against SARS-CoV-2. Most of the ACE2 inhibitors discovered till now are chemical synthesis; suffer from many limitations related to stability and adverse side effects. However, natural, and selective ACE2 inhibitors that possess strong stability and low side effects can be replaced instead of those chemicals' inhibitors. To envisage structurally diverse natural entities as an ACE2 inhibitor with better efficacy, a 3D structure-based-pharmacophore model (SBPM) has been developed and validated by 20 known selective inhibitors with their correspondence 1166 decoy compounds. The validated SBPM has excellent goodness of hit score and good predictive ability, which has been appointed as a query model for further screening of 11,295 natural compounds. The resultant 23 hits compounds with pharmacophore fit score 75.31 to 78.81 were optimized using in-silico ADMET and molecular docking analysis. Four potential natural inhibitory molecules namely D-DOPA (Amb17613565), L-Saccharopine (Amb6600091), D-Phenylalanine (Amb3940754), and L-Mimosine (Amb21855906) have been selected based on their binding affinity (−7.5, −7.1, −7.1, and −7.0 kcal/mol), respectively. Moreover, 250 ns molecular dynamics (MD) simulations confirmed the structural stability of the ligands within the protein. Additionally, MM/GBSA approach also used to support the stability of molecules to the binding site of the protein that also confirm the stability of the selected four natural compounds. The virtual screening strategy used in this study demonstrated four natural compounds that can be utilized for designing a future class of potential natural ACE2 inhibitor that will block the spike (S) protein dependent entry of SARS-CoV-2 into the host cell.

Transporter associated with antigen processing 1 (TAP1) expression and prognostic analysis in breast, lung, liver, and ovarian cancer

Abstract

Transporter associated with antigen processing 1 (TAP1) is a transporter protein that represent tumor antigen in the MHC I or HLA complex. Any defect in the TAP1 gene resulting in inadequate tumor tracking. TAP1 influences multidrug resistance (MDR) in human cancer cell lines and hinders the treatment during chemotherapeutic. The association of TAP1 in cancer progression remains mostly unknown and further study of the gene in relation with cancer need to conduct. Thus, the study has designed to analyze the association between the TAP1 with cancer by computationally. The expression pattern of the gene has determined by using ONCOMINE, GENT2, and GEPIA2 online platforms. The protein level of TAP1 was examined by the help of Human Protein Atlas. Samples with different clinical outcomes were investigated to evaluate the expression and promoter methylation in cancer vs. normal tissues by using UALCAN server. The copy number alteration, mutation frequency, and expression level of the gene in different cancer were analyzed by using cBioPortal server. The PrognoScan and KM plotter platforms were used to perform the survival analysis and represented graphically. Additionally, pathway and gene ontology (GO) features correlated to the TAP1 gene were analyzed and presented by bar charts. After arranging the data in a single panel like correlating expression to prognosis, mutational and alterations characteristic, and pathways analysis, we observed some interesting insights that emphasized the importance of the gene in cancer progression. The study found the relationship between the TAP1 expression pattern and prognosis in different cancer tissues and shows how TAP1 affects the clinical characteristics. The analytical data presented in the study is vital to learn about the effect of TAP1 in tumor tissue, where previously studies showing contradicting expression of TAP1 in cancer tissue. The analyzed data can also be utilized further to evade the threats against chemotherapy. Overall, the study provided a new aspect to consider the role of TAP1 gene in cancer progression and survival status.

Key messages

• This study demonstrated, for the first time, a correlation between the TAP1 gene and tumor progression.

• An upregulation of TAP1 mRNA was demonstrated in various cancer types.

• This study reported a significant negative correlation for TAP1 gene expression and the survival rate in different cancer types.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00109-021-02088-w.

Epidermal Integrin α3β1 Regulates Tumor-Derived Proteases BMP-1, Matrix Metalloprotease-9, and Matrix Metalloprotease-3

As the major cell surface receptors for the extracellular matrix, integrins regulate adhesion and migration and have been shown to drive tumor growth and progression. Previous studies showed that mice lacking integrin α3β1 in the epidermis fail to form skin tumors during two-step chemical tumorigenesis, indicating a protumorigenic role for α3β1. Furthermore, genetic ablation of α3β1 in established skin tumors caused their rapid regression, indicating an essential role in the maintenance of tumor growth. In this study, analysis of immortalized keratinocyte lines and their conditioned media support a role for α3β1 in regulating the expression of several extracellular proteases of the keratinocyte secretome, namely BMP-1, matrix metalloprotease (MMP)-9, and MMP-3. Moreover, immunofluorescence revealed reduced levels of each protease in α3β1-deficient tumors, and RNA in situ hybridization showed that their expression was correspondingly reduced in α3β1-deficient tumor cells in vivo. Bioinformatic analysis confirmed that the expression of BMP1, MMP9, and MMP3 genes correlate with the expression of ITGA3 (gene encoding the integrin α3 subunit) in human squamous cell carcinoma and that high ITGA3 and MMP3 associate with poor survival outcome in these patients. Overall, our findings identify α3β1 as a regulator of several proteases within the secretome of epidermal tumors and as a potential therapeutic target.

Pharmacoinformatics and molecular dynamics simulation-based phytochemical screening of neem plant (Azadiractha indica) against human cancer by targeting MCM7 protein

Minichromosome maintenance complex component 7 (MCM7) belongs to the minichromosome maintenance family that is important for the initiation of eukaryotic DNA replication. Overexpression of the MCM7 protein is relative to cellular proliferation and responsible for aggressive malignancy in various cancers. Mechanistically, inhibition of MCM7 significantly reduces the cellular proliferation associated with cancer. To date, no effective small molecular candidate has been identified that can block the progression of cancer induced by the MCM7 protein. Therefore, the study has been designed to identify small molecular-like natural drug candidates against aggressive malignancy associated with various cancers by targeting MCM7 protein. To identify potential compounds against the targeted protein a comprehensive in silico drug design including molecular docking, ADME (Absorption, Distribution, Metabolism and Excretion), toxicity, and molecular dynamics (MD) simulation approaches has been applied. Seventy phytochemicals isolated from the neem tree (Azadiractha indica) were retrieved and screened against MCM7 protein by using the molecular docking simulation method, where the top four compounds have been chosen for further evaluation based on their binding affinities. Analysis of ADME and toxicity properties reveals the efficacy and safety of the selected four compounds. To validate the stability of the protein-ligand complex structure MD simulations approach has also been performed to the protein-ligand complex structure, which confirmed the stability of the selected three compounds including CAS ID:105377-74-0, CID:12308716 and CID:10505484 to the binding site of the protein. In the study, a comprehensive data screening process has performed based on the docking, ADMET properties, and MD simulation approaches, which found a good value of the selected four compounds against the targeted MCM7 protein and indicates as a promising and effective human anticancer agent.

Structure based pharmacophore modeling, virtual screening, molecular docking and ADMET approaches for identification of natural anti-cancer agents targeting XIAP protein

X-linked inhibitor of apoptosis protein (XIAP) is a member of inhibitor of apoptosis protein (IAP) family responsible for neutralizing the caspases-3, caspases-7, and caspases-9. Overexpression of the protein decreased the apoptosis process in the cell and resulting development of cancer. Different types of XIAP antagonists are generally used to repair the defective apoptosis process that can eliminate carcinoma from living bodies. The chemically synthesis compounds discovered till now as XIAP inhibitors exhibiting side effects, which is making difficulties during the treatment of chemotherapy. So, the study has design to identifying new natural compounds that are able to induce apoptosis by freeing up caspases and will be low toxic. To identify natural compound, a structure-based pharmacophore model to the protein active site cavity was generated following by virtual screening, molecular docking and molecular dynamics (MD) simulation. Initially, seven hit compounds were retrieved and based on molecular docking approach four compounds has chosen for further evaluation. To confirm stability of the selected drug candidate to the target protein the MD simulation approach were employed, which confirmed stability of the three compounds. Based on the finding, three newly obtained compounds namely Caucasicoside A (ZINC77257307), Polygalaxanthone III (ZINC247950187), and MCULE-9896837409 (ZINC107434573) may serve as lead compounds to fight against the treatment of XIAP related cancer, although further evaluation through wet lab is necessary to measure the efficacy of the compounds.