Bioinformatics prediction and analysis of hub genes and pathways of three types of gynecological cancer

Exploring SSR1 as a novel diagnostic and prognostic biomarker in hepatocellular carcinoma, and its relationship with immune infiltration

Background

Although signal sequence receptor subunit 1 (SSR1) has undergone thorough examination in different cancer types, its importance in hepatocellular carcinoma (HCC) remains largely uncharted and warrants further investigation. The aim of this study is to explore the role of SSR1 in HCC progression and to decipher its underlying molecular mechanisms.

Methods

We employed the ONCOMINE, Tumor IMmune Estimation Resource (TIMER), and The Cancer Genome Atlas databases to assess SSR1 expression levels within tumor tissues. Logistic and Cox regression analyses, Kaplan-Meier survival plots, nomograms, and forest plots were employed to establish correlation between SSR1 and prognosis. Receiver operating characteristic (ROC) curves demonstrated diagnostic utility of SSR1. Additionally, Gene Ontology (GO) and gene set enrichment analysis (GSEA) analyses were conducted to uncover relevant molecular pathways. TIMER was instrumental in elucidating the connection between SSR1 and immune cell infiltration. Actions of SSR1 in HCC proliferation and migration were investigated through quantitative real-time polymerase chain reaction, Cell Counting Kit-8, 5-ethynyl-2'-deoxyuridine cell proliferation assays, and Transwell migration and wound healing experiments.

Results

Elevated SSR1 levels were found to be correlated with clinical parameters such as age and pathologic stage, thereby predicting a reduced overall survival (OS) rate in HCC patients. Multivariate survival analysis underscored SSR1 as an independent prognostic marker for OS. A nomogram underscored SSR1's effectiveness as a predictive tool for HCC outcomes, while ROC analysis indicated its high diagnostic accuracy. GO and GSEA analyses suggested that elevated SSR1 expression may be associated with epithelial-mesenchymal transition (EMT) pathway. SSR1 exhibited a negative correlation with cytotoxic cells and a positive correlation with Th2 cells. Our in vitro experiments provided evidence that heightened SSR1 levels may impact HCC proliferation and migration through EMT pathway.

Conclusions

SSR1 surfaces as a new diagnostic and potentially prognostic biomarker, showing an association with immune cell infiltration and cell proliferation in HCC.

Unraveling malignant phenotype of peritumoral tissue: transcriptomic insights into early-stage breast cancer

BACKGROUND

Early-stage invasive ductal carcinoma displays high survival rates due to early detection and treatments. However, there is still a chance of relapse of 3-15% after treatment. The aim of this study was to uncover the distinctive transcriptomic characteristics and monitoring prognosis potential of peritumoral tissue in early-stage cases.

METHODS

RNA was isolated from tumoral, peritumoral, and non-tumoral breast tissue from surgical resection of 10 luminal early-stage invasive ductal carcinoma patients. Transcriptome expression profiling for differentially expressed genes (DEGs) identification was carried out through microarray analysis. Gene Ontology and KEGG pathways enrichment analysis were explored for functional characterization of identified DEGs. Protein-Protein Interactions (PPI) networks analysis was performed to identify hub nodes of peritumoral tissue alterations and correlated with Overall Survival and Relapse Free Survival.

RESULTS

DEGs closely related with cell migration, extracellular matrix organization, and cell cycle were upregulated in peritumoral tissue compared to non-tumoral. Analyzing PPI networks, we observed that the proximity to tumor leads to the alteration of gene modules involved in cell proliferation and differentiation signaling pathways. In fact, in the peritumoral area were identified the top ten upregulated hub nodes including CDK1, ESR1, NOP58, PCNA, EZH2, PPP1CA, BUB1, TGFBR1, CXCR4, and CCND1. A signature performed by four of these hub nodes (CDK1, PCNA, EZH2, and BUB1) was associated with relapse events in untreated luminal breast cancer patients.

CONCLUSIONS

In conclusion, our study characterizes in depth breast peritumoral tissue providing clues on the changes that tumor signaling could cause in patients with early-stage breast cancer. We propose that the use of a four gene signature could help to predict local relapse. Overall, our results highlight the value of peritumoral tissue as a potential source of new biomarkers for early detection of relapse and improvement in invasive ductal carcinoma patient's prognosis.

In silico based analysis to explore genetic linkage between atherosclerosis and its potential risk factors

Atherosclerosis (ATH) is a chronic cardiovascular disease characterized by plaque formation in arteries, and it is a major cause of illness and death. Although therapeutic advances have significantly improved the prognosis of ATH, missing therapeutic targets pose a significant residual threat. This research used a systems biology approach to identify the molecular biomarkers involved in the onset and progression of ATH, analysing microarray gene expression datasets from ATH and tissues impacted by risk factors such as high cholesterol, adipose tissue, smoking, obesity, sedentary lifestyle, stress, alcohol consumption, hypertension, hyperlipidaemia, high fat, diabetes to find the differentially expressed genes (DEGs). Bioinformatic analyses of Protein-Protein Interaction (PPI), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) were conducted on differentially expressed genes, revealing metabolic and signaling pathways (the chemokine signaling pathway, cytokine-cytokine receptor interaction, the cytosolic DNA-sensing pathway, the peroxisome proliferator-activated receptors signaling pathway, and the nuclear factor-kappa B signaling pathway), ten hubs proteins (CCL5, CCR1, TLR1, CCR2, FCGR2A, IL1B, CD163, AIF1, CXCL-1 and TNF), five transcription factors (YY1, FOXL1, FOXC1, SRF, and GATA2), and five miRNAs (mir-27a-3p, mir-124-3p, mir-16-5p, mir-129-2-3p, mir-1-3p). These findings identify potential biomarkers that may increase knowledge of the mechanisms underlying ATH and their connection to risk factors, aiding in the development of new therapies.

Identification of miR-3182 and miR-3143 target genes involved in the cell cycle as a novel approach in TNBC treatment: A systems biology approach

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with a poor prognosis, lacking therapeutic targets. miRNAs play crucial roles in TNBC through regulating various mechanisms, including cellular growth and proliferation. This study aims to identify critical target genes of two novel miRNAs (miR-3143 and miR-3182) involved in the cell cycle of TNBC as possible therapeutic targets and investigates their regulatory and therapeutic roles through a systems biology approach and in vitro experiment. Datasets related to the TNBC cell line (MDA-MB-231) were screened and retrieved, and Gene regulatory networks were constructed. Significant regulatory motifs were detected and analyzed using the FANMOD and Cytoscape analyzer, and the clusters and seeds were identified using the MCODE. Functional enrichment analysis was also performed using DAVID and STRING. The most critical genes were determined using the analysis of GRN motifs and PPI clusters. The essential genes involved in the cell cycle were selected and verified using the bc-GenExMiner v4.7. We overexpressed miR-3143 and miR-3182 in the MDA-MB-231 cell line using human umbilical cord mesenchymal stem cell (HUCMSC)-miRNA loaded exosomes, and the expression of the critical target genes was investigated using RT-qPCR. We identified eight critical genes as potential therapeutic targets. Their expression decreased by overexpression of miR-3143 and miR-3182 in RT-qPCR. The identified critical genes have probably significant roles in the pathogenesis of TNBC through the cell cycle. We suggest that the overexpression of miR-3143 and miR-3182 could be a new therapeutic candidate in TNBC and is worth more investigation.

Deciphering cervical cancer-associated biomarkers by integrated multi-omics approach

OBJECTIVES

Cervical Squamous Cell Carcinoma (CESC) is one of the most fatal female malignancies, and the underlying molecular mechanisms governing this disease have not been fully explored. In this research, we planned to conduct the analysis of Gene Expression Omnibus (GEO) cervical squamous cell carcinoma microarray datasets by a detailed in silico approach and to explore some novel biomarkers of CESC.

METHODS

The top commonly differentially expressed genes (DEGs) from the GSE138080 and GSE113942 datasets were analyzed by Limma package-based GEO2R tool. The protein-protein interaction (PPI) network of the DEGs was drawn through Search Tool for the Retrieval of Interacting Genes (STRING), and top 6 hub genes were obtained from Cytoscape. Expression analysis and validation of hub genes expression in CESC samples and cell lines were done using UALCAN, OncoDB, GENT2, and HPA. Additionally, cBioPortal, Gene set enrichment analysis (GSEA) tool, Kaplan-Meier (KM) plotter, ShinyGO, and DGIdb databases were also used to check some important values of hub genes in CESC.

RESULTS

Out of 79 DEGs, the minichromosome maintenance complex component 4 (MCM4), nucleolar and spindle-associated protein 1 (NUSAP1), cell division cycle associated 5 (CDCA5), cell division cycle 45 (CDC45), denticleless E3 ubiquitin protein ligase homolog (DTL), and chromatin licensing and DNA replication factor 1 (CDT1) genes were regarded as hub genes in CESC. Further analysis revealed that the expressions of all these hub genes were significantly elevated in CESC cell lines and samples of diverse clinical attributes. In this study, we also documented some important correlations between hub genes and some other diverse measures, including DNA methylation, genetic alterations, and Overall Survival (OS). Last, we also identify hub genes associated ceRNA network and 31 important chemotherapeutic drugs.

CONCLUSION

Through detailed in silico methodology, we identified 6 hub genes, including MCM4, NUSAP1, CDCA5, CDC45, DTL, and CDT1, which are likely to be associated with CESC development and diagnosis.

A critical review of datasets and computational suites for improving cancer theranostics and biomarker discovery

Cancer has been constantly evolving and so is the research pertaining to cancer diagnosis and therapeutic regimens. Early detection and specific therapeutics are the key features of modern cancer therapy. These requirements can only be fulfilled with the integration of diverse high-throughput technologies. Integration of advanced omics methodology involving genomics, epigenomics, proteomics, and transcriptomics provide a clear understanding of multi-faceted cancer. In the past few years, tremendous high-throughput data have been generated from cancer genomics and epigenomic analyses, which on further methodological analyses can yield better biological insights. The major epigenetic alterations reported in cancer are DNA methylation levels, histone post-translational modifications, and epi-miRNA regulating the oncogenes and tumor suppressor genes. While the genomic analyses like gene expression profiling, cancer gene prediction, and genome annotation divulge the genetic alterations in oncogenes or tumor suppressor genes. Also, systems biology approach using biological networks is being extensively used to identify novel cancer biomarkers. Therefore, integration of these multi-dimensional approaches will help to identify potential diagnostic and therapeutic biomarkers. Here, we reviewed the critical databases and tools dedicated to various epigenomic and genomic alterations in cancer. The review further focuses on the multi-omics resources available for further validating the identified cancer biomarkers. We also highlighted the tools for cancer biomarker discovery using a systems biology approach utilizing genomic and epigenomic data. Biomarkers predicted using such integrative approaches are shown to be more clinically relevant.

Metadata analysis to explore hub of the hub-genes highlighting their functions, pathways and regulators for cervical cancer diagnosis and therapies

Cervical cancer (CC) is considered as the fourth most common women cancer globally.that shows malignant features of local infiltration and invasion into adjacent organs and tissues. There are several individual studies in the literature that explored CC-causing hub-genes (HubGs), however, we observed that their results are not so consistent. Therefore, the main objective of this study was to explore hub of the HubGs (hHubGs) that might be more representative CC-causing HubGs compare to the single study based HubGs. We reviewed 52 published articles and found 255 HubGs/studied-genes in total. Among them, we selected 10 HubGs (CDK1, CDK2, CHEK1, MKI67, TOP2A, BRCA1, PLK1, CCNA2, CCNB1, TYMS) as the hHubGs by the protein-protein interaction (PPI) network analysis. Then, we validated their differential expression patterns between CC and control samples through the GPEA database. The enrichment analysis of HubGs revealed some crucial CC-causing biological processes (BPs), molecular functions (MFs) and cellular components (CCs) by involving hHubGs. The gene regulatory network (GRN) analysis identified four TFs proteins and three miRNAs as the key transcriptional and post-transcriptional regulators of hHubGs. Then, we identified hHubGs-guided top-ranked FDA-approved 10 candidate drugs and validated them against the state-of-the-arts independent receptors by molecular docking analysis. Finally, we investigated the binding stability of the top-ranked three candidate drugs (Docetaxel, Temsirolimus, Paclitaxel) by using 100 ns MD-based MM-PBSA simulations and observed their stable performance. Therefore the finding of this study might be the useful resources for CC diagnosis and therapies.

Highly perturbed genes and hub genes associated with type 2 diabetes in different tissues of adult humans: a bioinformatics analytic workflow

Type 2 diabetes (T2D) has a complex etiology which is not yet fully elucidated. The identification of gene perturbations and hub genes of T2D may deepen our understanding of its genetic basis. We aimed to identify highly perturbed genes and hub genes associated with T2D via an extensive bioinformatics analytic workflow consisting of five steps: systematic review of Gene Expression Omnibus and associated literature; identification and classification of differentially expressed genes (DEGs); identification of highly perturbed genes via meta-analysis; identification of hub genes via network analysis; and downstream analysis of highly perturbed genes and hub genes. Three meta-analytic strategies, random effects model, vote-counting approach, and p value combining approach, were applied. Hub genes were defined as those nodes having above-average betweenness, closeness, and degree in the network. Downstream analyses included gene ontologies, Kyoto Encyclopedia of Genes and Genomes pathways, metabolomics, COVID-19-related gene sets, and Genotype-Tissue Expression profiles. Analysis of 27 eligible microarrays identified 6284 DEGs (4592 downregulated and 1692 upregulated) in four tissue types. Tissue-specific gene expression was significantly greater than tissue non-specific (shared) gene expression. Analyses revealed 79 highly perturbed genes and 28 hub genes. Downstream analyses identified enrichments of shared genes with certain other diabetes phenotypes; insulin synthesis and action-related pathways and metabolomics; mechanistic associations with apoptosis and immunity-related pathways; COVID-19-related gene sets; and cell types demonstrating over- and under-expression of marker genes of T2D. Our approach provided valuable insights on T2D pathogenesis and pathophysiological manifestations. Broader utility of this pipeline beyond T2D is envisaged.

Bioinformatics Screening of Potential Biomarkers from mRNA Expression Profiles to Discover Drug Targets and Agents for Cervical Cancer

Bioinformatics analysis has been playing a vital role in identifying potential genomic biomarkers more accurately from an enormous number of candidates by reducing time and cost compared to the wet-lab-based experimental procedures for disease diagnosis, prognosis, and therapies. Cervical cancer (CC) is one of the most malignant diseases seen in women worldwide. This study aimed at identifying potential key genes (KGs), highlighting their functions, signaling pathways, and candidate drugs for CC diagnosis and targeting therapies. Four publicly available microarray datasets of CC were analyzed for identifying differentially expressed genes (DEGs) by the LIMMA approach through GEO2R online tool. We identified 116 common DEGs (cDEGs) that were utilized to identify seven KGs (AURKA, BRCA1, CCNB1, CDK1, MCM2, NCAPG2, and TOP2A) by the protein–protein interaction (PPI) network analysis. The GO functional and KEGG pathway enrichment analyses of KGs revealed some important functions and signaling pathways that were significantly associated with CC infections. The interaction network analysis identified four TFs proteins and two miRNAs as the key transcriptional and post-transcriptional regulators of KGs. Considering seven KGs-based proteins, four key TFs proteins, and already published top-ranked seven KGs-based proteins (where five KGs were common with our proposed seven KGs) as drug target receptors, we performed their docking analysis with the 80 meta-drug agents that were already published by different reputed journals as CC drugs. We found Paclitaxel, Vinorelbine, Vincristine, Docetaxel, Everolimus, Temsirolimus, and Cabazitaxel as the top-ranked seven candidate drugs. Finally, we investigated the binding stability of the top-ranked three drugs (Paclitaxel, Vincristine, Vinorelbine) by using 100 ns MD-based MM-PBSA simulations with the three top-ranked proposed receptors (AURKA, CDK1, TOP2A) and observed their stable performance. Therefore, the proposed drugs might play a vital role in the treatment against CC.

Bioinformatics Characterization of Candidate Genes Associated with Gene Network and miRNA Regulation in Esophageal Squamous Cell Carcinoma Patients

The present study aimed to identify potential therapeutic targets for esophageal squamous cell carcinoma (ESCC). The gene expression profile GSE161533 contained 84 samples, in that 28 tumor tissues and 28 normal tissues encoded as ESCC patients were retrieved from the Gene Expression Omnibus database. The obtained data were validated and screened for differentially expressed genes (DEGs) between normal and tumor tissues with the GEO2R tool. Next, the protein–protein network (PPI) was constructed using the (STRING 2.0) and reconstructed with Cytoscape 3.8.2, and the top ten hub genes (HGsT10) were predicted using the Maximal Clique Centrality (MCC) algorithm of the CytoHubba plugin. The identified hub genes were mapped in GSE161533, and their expression was determined and compared with The Cancer Genome Atlas (TCGA.) ESCC patient’s samples. The overall survival rate for HGsT10 wild and mutated types was analyzed with the Gene Expression Profiling Interactive Analysis2 (GEPIA2) server and UCSC Xena database. The functional and pathway enrichment analysis was performed using the WebGestalt database with the reference gene from lumina human ref 8.v3.0 version. The promoter methylation for the HGsT10 was identified using the UALCAN server. Additionally, the miRNA-HGsT10 regulatory network was constructed to identify the top ten hub miRNAs (miRT10). Finally, we identified the top ten novel driving genes from the DEGs of GSE161533 ESCC patient’s sample using a multi-omics approach. It may provide new insights into the diagnosis and treatment for the ESCC affected patients early in the future.

SOX9: Advances in Gynecological Malignancies

Transcription factors of the SOX family were first discovered in mammals in 1990. The sex-determining region Y box 9 belongs to the SOX transcription factor family. It plays an important role in inducing tissue and cell morphogenesis, survival, and many developmental processes. Furthermore, it has been shown to be an oncogene in many tumors. Gynecological malignancies are tumors that occur in the female reproductive system and seriously threaten the lives of patients. Common gynecological malignancies include ovarian cancer, cervical cancer, and endometrial cancer. So far, the molecular mechanisms related to the incidence and development of gynecological malignancies remain unclear. This makes it particularly important to discover their common causative molecule and thus provide an effective therapeutic target. In recent years, studies have found that multiple mechanisms are involved in regulating the expression of the sex-determining region Y box 9, leading to the occurrence and development of gynecological malignancies. In this review, we discuss the prognostic value of SOX9 expression and the potential of targeting SOX9 for gynecological malignancy treatment. We also discuss progress regarding the role of SOX9 in gynecological malignancy pathogenesis through its mediation of important mechanisms, including tumor initiation and proliferation, apoptosis, migration, invasion, chemoresistance, and stem cell maintenance.

Clinical Value for Diagnosis and Prognosis of Signal Sequence Receptor 1 (SSR1) and Its Potential Mechanism in Hepatocellular Carcinoma: A Comprehensive Study Based on High-Throughput Data Analysis

Objective

Hepatocellular Carcinoma (HCC) has the characteristics of high incidence and poor prognosis. However, the underlying mechanism of HCC has not yet been fully elucidated. This study aims to investigate the potential mechanism and clinical significance of signal sequence receptor (SSR1) in HCC through bioinformatics methods.

Methods

Four online (GEPIA, TIMER, TCGA, and GEO) databases were used to explore the expression level of SSR1 in HCC. The summary receiver operating characteristic (SROC) analysis and standardized mean difference (SMD) calculation were performed further to detect its diagnostic ability and expression level. The Human Protein Atlas (HPA) database was applied to verify the level of SSR1 protein expression. Chi-square test and Fisher’s exact test were carried out to determine the clinical relevance of SSR1 expression. KM survival analysis, univariate and multivariate COX regression analyses were employed to explore the prognostic impact of SSR1. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene set enrichment analysis (GSEA) were implemented to reveal the underlying mechanism of SSR1. Quantitative Real-Time Polymerase Chain Reaction (QRT-PCR) was used to verify the expression of SSR1 in HCC.

Results

SSR1 was significantly overexpressed in HCC (SMD=1.25, P=0.03) and had the moderate diagnostic ability (AUC=0.84). SSR1 expression was significantly correlated with T stage, Gender, Pathologic stage (All P<0.05). Patients with high SSR1 expression had shorter overall survival (OS). Univariate and multivariate Cox regression analyses showed that high SSR1 expression was an independent risk factor for poor prognosis. KEGG analysis showed that SSR1-related genes were enriched in the cell cycle, DNA replication, and TGF-beta signaling pathway. GSEA analysis also shows that the high expression of SSR1 is related to the activation of the above three signal pathways. qRT-PCR showed that the SSR1 expression in HCC was significantly higher than the Peri-carcinoma tissue (PHCC) and the corresponding normal liver tissue.

Conclusion

SSR1 expression was significantly up-regulated, and it had the potential as a biomarker for the diagnosis and prognosis of HCC. It was very likely to participate in the occurrence and development of HCC by regulating the cell cycle. In summary, our study comprehensively analyzed the clinical value of SSR1 and also conducted a preliminary study on its potential mechanism, which will provide inspiration for the in-depth study of SSR1 in HCC.

Pathogenic nsSNPs that increase the risks of cancers among the Orang Asli and Malays

Single-nucleotide polymorphisms (SNPs) are the most common genetic variations for various complex human diseases, including cancers. Genome-wide association studies (GWAS) have identified numerous SNPs that increase cancer risks, such as breast cancer, colorectal cancer, and leukemia. These SNPs were cataloged for scientific use. However, GWAS are often conducted on certain populations in which the Orang Asli and Malays were not included. Therefore, we have developed a bioinformatic pipeline to mine the whole-genome sequence databases of the Orang Asli and Malays to determine the presence of pathogenic SNPs that might increase the risks of cancers among them. Five different in silico tools, SIFT, PROVEAN, Poly-Phen-2, Condel, and PANTHER, were used to predict and assess the functional impacts of the SNPs. Out of the 80 cancer-related nsSNPs from the GWAS dataset, 52 nsSNPs were found among the Orang Asli and Malays. They were further analyzed using the bioinformatic pipeline to identify the pathogenic variants. Three nsSNPs; rs1126809 (TYR), rs10936600 (LRRC34), and rs757978 (FARP2), were found as the most damaging cancer pathogenic variants. These mutations alter the protein interface and change the allosteric sites of the respective proteins. As TYR, LRRC34, and FARP2 genes play important roles in numerous cellular processes such as cell proliferation, differentiation, growth, and cell survival; therefore, any impairment on the protein function could be involved in the development of cancer. rs1126809, rs10936600, and rs757978 are the important pathogenic variants that increase the risks of cancers among the Orang Asli and Malays. The roles and impacts of these variants in cancers will require further investigations using in vitro cancer models.

Expression Profiles Reveal Involvement of VEGF, IGF1, BIRC5, and MMP1 in Vulvar Carcinogenesis

OBJECTIVE

The objective of this study was to identify key genes and shed light on the underlying molecular mechanisms of vulvar squamous cell carcinoma (VSCC).

METHODS

Bioinformatic software was utilized for the identification and characterization of key differentially expressed genes (DEGs) from microarrays GSE63678 and GSE38228, which contain VSCC and normal vulvar tissue data. These microarrays were obtained from Gene Expression Omnibus (GEO). Immunohistochemical assays (55 VSCC and 50 normal vulvar tissues) were utilized to validate the expression of VEGF, IGF1, BIRC5, and MMP1 screened from the identified DEGs. SPSS 18.0 software was used for statistical analyses of the relationships between IGF1, BIRC5, VEGF, MMP1 expression levels and patient clinicopathological characteristics.

RESULTS

A total of 141 DEGs were identified, among which 18 genes were closely correlated with the biological characteristics of VSCC. Four of the 18 genes (VEGF, IGF1, BIRC5, and MMP1) screened from the GEO database were markedly enriched in pathways in cancer (P < 0.05), and could be considered key genes in VSCC based on KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis in DAVID (Database for Annotation, Visualization and Integrated Discovery).The expression levels of these 4 hub genes, determined by immunohistochemical assays, were consistent with the bioinformatics results. Higher expression of IGF1 showed significant association with well-differentiated carcinomas (P = 0.017).BIRC5 expression levels showed a positive correlation with clinical stage (P = 0.039); compared with those in menopause for over 10 years, patients in menopause for less than 10 years at the time of diagnosis tended to have significantly higher expression of BIRC5 (P = 0.003). VEGF and MMP1 expression levels were not correlated with any of the tested clinicopathological characteristics.

CONCLUSION

VEGF, IGF1, BIRC5, and MMP1 were identified as being associated with VSCC using integrated bioinformatic methods, which may provide important insights into the pathogenesis of this disease and help to identify new biomarkers.

Computational approach to decipher cellular interactors and drug targets during co-infection of SARS-CoV-2, Dengue, and Chikungunya virus

The world is reeling under severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, and it will be frightening if compounded by other co-existing infections. The co-occurrence of the Dengue virus (DENV) and Chikungunya virus (CHIKV) has been into existence, but recently the co-infection of DENV and SARS-CoV-2 has been reported. Thus, the possibility of DENV, CHIKV, and SARS-CoV-2 co-infection could be predicted in the future with enhanced vulnerability. It is essential to elucidate the host interactors and the connected pathways to understand the biological insights. The in silico approach using Cytoscape was exploited to elucidate the common human proteins interacting with DENV, CHIKV, and SARS-CoV-2 during their probable co-infection. In total, 17 interacting host proteins were identified showing association with envelope, structural, non-structural, and accessory proteins. Investigating the functional and biological behaviour using PANTHER, UniProtKB, and KEGG databases uncovered their association with several cellular pathways including, signaling pathways, RNA processing and transport, cell cycle, ubiquitination, and protein trafficking. Withal, exploring the DrugBank and Therapeutic Target Database, total seven druggable host proteins were predicted. Among all integrin beta-1, histone deacetylase-2 (HDAC2) and microtubule affinity-regulating kinase-3 were targeted by FDA approved molecules/ drugs. Furthermore, HDAC2 was predicted to be the most significant target, and some approved drugs are available against it. The predicted druggable targets and approved drugs could be investigated to obliterate the identified interactions that could assist in inhibiting viral infection.

CDK1 serves as a novel therapeutic target for endometrioid endometrial cancer

Background: Endometrial cancer (EC) is one of the most common and prevalent gynecologic malignancies worldwide. The aim of this study was to identify a novel therapeutic target for endometrioid endometrial cancer.

Materials and Methods: Bioinformatic analysis was performed and CDK1 was screen out as one of the hub genes in the pathogenesis of EC. Immunohistochemistry was used to verify the expression of CDK1 in endometrial cancer tissue. Cell viability and colony formation were used to study the effects of CDK1 on the proliferation and colony formation of endometrial cancer cells in vitro. Apoptosis and cell cycle assays were used to elucidate the mechanism of CDK1 affecting cell proliferation. Tumor xenograft transplantation assay was performed to show the effects of CDK1 on the growth of endometrial cancer cells in vivo.

Results: CDK1 was over expressed in endometrioid endometrial cancer, and accumulation of cytoplasmic CDK1 was associated with histological grade of EC. CDK1 promoted endometrial cancer cell growth and colony formation in vitro. The inhibition of CDK1 activity induced cell apoptosis and caused G2/M phase arrest of cell cycle in endometrial cancer cells. The inhibition of CDK1 activity also inhibited endometrial cancer growth in xenograft models.

Conclusion: CDK1 was involved in the pathogenesis of endometrioid endometrial cancer and provided a novel therapeutic target for endometrioid endometrial cancer.

Investigating the Role of Telomere and Telomerase Associated Genes and Proteins in Endometrial Cancer

Endometrial cancer (EC) is the commonest gynaecological malignancy. Current prognostic markers are inadequate to accurately predict patient survival, necessitating novel prognostic markers, to improve treatment strategies. Telomerase has a unique role within the endometrium, whilst aberrant telomerase activity is a hallmark of many cancers. The aim of the current in silico study is to investigate the role of telomere and telomerase associated genes and proteins (TTAGPs) in EC to identify potential prognostic markers and therapeutic targets. Analysis of RNA-seq data from The Cancer Genome Atlas identified differentially expressed genes (DEGs) in EC (568 TTAGPs out of 3467) and ascertained DEGs associated with histological subtypes, higher grade endometrioid tumours and late stage EC. Functional analysis demonstrated that DEGs were predominantly involved in cell cycle regulation, while the survival analysis identified 69 DEGs associated with prognosis. The protein-protein interaction network constructed facilitated the identification of hub genes, enriched transcription factor binding sites and drugs that may target the network. Thus, our in silico methods distinguished many critical genes associated with telomere maintenance that were previously unknown to contribute to EC carcinogenesis and prognosis, including NOP56, WFS1, ANAPC4 and TUBB4A. Probing the prognostic and therapeutic utility of these novel TTAGP markers will form an exciting basis for future research.