Integrative Systems Biology Approaches to Identify Potential Biomarkers and Pathways of Cervical Cancer

System biology approach to identify the novel biomarkers in glioblastoma multiforme tumors by using computational analysis

Introduction: The most common primary brain tumor in adults is glioblastoma multiforme (GBM), accounting for 45.2% of all cases. The characteristics of GBM, a highly aggressive brain tumor, include rapid cell division and a propensity for necrosis. Regretfully, the prognosis is extremely poor, with only 5.5% of patients surviving after diagnosis. Methodology: To eradicate these kinds of complicated diseases, significant focus is placed on developing more effective drugs and pinpointing precise pharmacological targets. Finding appropriate biomarkers for drug discovery entails considering a variety of factors, including illness states, gene expression levels, and interactions between proteins. Using statistical techniques like p-values and false discovery rates, we identified differentially expressed genes (DEGs) as the first step in our research for identifying promising biomarkers in GBM. Of the 132 genes, 13 showed upregulation, and only 29 showed unique downregulation. No statistically significant changes in the expression of the remaining genes were observed. Results: Matrix metallopeptidase 9 (MMP9) had the greatest degree in the hub biomarker gene identification, followed by (periostin (POSTN) at 11 and Hes family BHLH transcription factor 5 (HES5) at 9. The significance of the identification of each hub biomarker gene in the initiation and advancement of glioblastoma multiforme was brought to light by the survival analysis. Many of these genes participate in signaling networks and function in extracellular areas, as demonstrated by the enrichment analysis.We also identified the transcription factors and kinases that control proteins in the proteinprotein interactions (PPIs) of the DEGs. Discussion: We discovered drugs connected to every hub biomarker. It is an appealing therapeutic target for inhibiting MMP9 involved in GBM. Molecular docking investigations indicated that the chosen complexes (carmustine, lomustine, marimastat, and temozolomide) had high binding affinities of -6.3, -7.4, -7.7, and -8.7 kcal/mol, respectively, the mean root-mean-square deviation (RMSD) value for the carmustine complex and marimastat complex was 4.2 Å and 4.9 Å, respectively, and the lomustine and temozolomide complex system showed an average RMSD of 1.2 Å and 1.6 Å, respectively. Additionally, high stability in root-mean-square fluctuation (RMSF) analysis was observed with no structural conformational changes among the atomic molecules. Thus, these in silico investigations develop a new way for experimentalists to target lethal diseases in future.

Multidimensional outlook on the pathophysiology of cervical cancer invasion and metastasis

Cervical cancer being one of the primary causes of high mortality rates among women is an area of concern, especially with ineffective treatment strategies. Extensive studies are carried out to understand various aspects of cervical cancer initiation, development and progression; however, invasive cervical squamous cell carcinoma has poor outcomes. Moreover, the advanced stages of cervical cancer may involve lymphatic circulation with a high risk of tumor recurrence at distant metastatic sites. Dysregulation of the cervical microbiome by human papillomavirus (HPV) together with immune response modulation and the occurrence of novel mutations that trigger genomic instability causes malignant transformation at the cervix. In this review, we focus on the major risk factors as well as the functionally altered signaling pathways promoting the transformation of cervical intraepithelial neoplasia into invasive squamous cell carcinoma. We further elucidate genetic and epigenetic variations to highlight the complexity of causal factors of cervical cancer as well as the metastatic potential due to the changes in immune response, epigenetic regulation, DNA repair capacity, and cell cycle progression. Our bioinformatics analysis on metastatic and non-metastatic cervical cancer datasets identified various significantly and differentially expressed genes as well as the downregulation of potential tumor suppressor microRNA miR-28-5p. Thus, a comprehensive understanding of the genomic landscape in invasive and metastatic cervical cancer will help in stratifying the patient groups and designing potential therapeutic strategies.

Current Status of 68Ga-Pentixafor in Solid Tumours

Chemokine receptor CXCR4 is overexpressed in neoplasms and its expression is related to tumour invasion, metastasis and aggressiveness. ⁶⁸Ga-Pentixafor is used to non-invasively image the expression of CXCR4 in tumours and has been widely used in haematological malignancies. Recent evidence shows that therapies targeting CXCR4 can increase the chemosensitivity of the tumour as well as inhibit tumour metastasis and aggressiveness. ⁶⁸Ga-Pentixafor has shown promise as an elegant radiotracer to aid in the selection of patients whose tumours demonstrate CXCR4 overexpression and who therefore may benefit from novel therapies targeting CXCR4. In addition, its therapeutic partners ¹⁷⁷Lu- and ⁹⁰Y-Pentixather have been investigated in the treatment of patients with advanced haematological malignancies, and initial studies have shown a good treatment response in metabolically active lesions. ⁶⁸Ga-Pentixafor in solid tumours complements ¹⁸F-FDG by providing prognostic information and selecting patients who may benefit from therapies targeting CXCR4. This review summarises the available literature on the potential applications of ⁶⁸Ga-Pentixafor in solid tumours.

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.

Immune-Related Protein Interaction Network in Severe COVID-19 Patients toward the Identification of Key Proteins and Drug Repurposing

Coronavirus disease 2019 (COVID-19) is still an active global public health issue. Although vaccines and therapeutic options are available, some patients experience severe conditions and need critical care support. Hence, identifying key genes or proteins involved in immune-related severe COVID-19 is necessary to find or develop the targeted therapies. This study proposed a novel construction of an immune-related protein interaction network (IPIN) in severe cases with the use of a network diffusion technique on a human interactome network and transcriptomic data. Enrichment analysis revealed that the IPIN was mainly associated with antiviral, innate immune, apoptosis, cell division, and cell cycle regulation signaling pathways. Twenty-three proteins were identified as key proteins to find associated drugs. Finally, poly (I:C), mitomycin C, decitabine, gemcitabine, hydroxyurea, tamoxifen, and curcumin were the potential drugs interacting with the key proteins to heal severe COVID-19. In conclusion, IPIN can be a good representative network for the immune system that integrates the protein interaction network and transcriptomic data. Thus, the key proteins and target drugs in IPIN help to find a new treatment with the use of existing drugs to treat the disease apart from vaccination and conventional antiviral therapy.

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.

A Bioinformatics-Assisted Review on Iron Metabolism and Immune System to Identify Potential Biomarkers of Exercise Stress-Induced Immunosuppression

The immune function is closely related to iron (Fe) homeostasis and allostasis. The aim of this bioinformatics-assisted review was twofold; (i) to update the current knowledge of Fe metabolism and its relationship to the immune system, and (ii) to perform a prediction analysis of regulatory network hubs that might serve as potential biomarkers during stress-induced immunosuppression. Several literature and bioinformatics databases/repositories were utilized to review Fe metabolism and complement the molecular description of prioritized proteins. The Search Tool for the Retrieval of Interacting Genes (STRING) was used to build a protein-protein interactions network for subsequent network topology analysis. Importantly, Fe is a sensitive double-edged sword where two extremes of its nutritional status may have harmful effects on innate and adaptive immunity. We identified clearly connected important hubs that belong to two clusters: (i) presentation of peptide antigens to the immune system with the involvement of redox reactions of Fe, heme, and Fe trafficking/transport; and (ii) ubiquitination, endocytosis, and degradation processes of proteins related to Fe metabolism in immune cells (e.g., macrophages). The identified potential biomarkers were in agreement with the current experimental evidence, are included in several immunological/biomarkers databases, and/or are emerging genetic markers for different stressful conditions. Although further validation is warranted, this hybrid method (human-machine collaboration) to extract meaningful biological applications using available data in literature and bioinformatics tools should be highlighted.