Novel Genomic Biomarker Candidates for Cervical Cancer As Identified by Differential Co-Expression Network Analysis

Multiomics Analysis and Machine Learning-based Identification of Molecular Signatures for Diagnostic Classification in Liver Disease Types Along the Microbiota-gut-liver Axis

Background

Liver disease, responsible for around two million deaths annually, remains a pressing global health challenge. Microbial interactions within the microbiota-gut-liver axis play a substantial role in the pathogenesis of various liver conditions, including early chronic liver disease (eCLD), chronic liver disease (CLD), acute liver failure (ALF), acute-on-chronic liver failure (ACLF), non-alcoholic fatty liver disease (NAFLD), steatohepatitis, and cirrhosis. This study aimed to identify key molecular signatures involved in liver disease progression by analyzing transcriptomic and gut microbiome data, and to evaluate their diagnostic utility using machine learning models.

Methods

Transcriptomic analysis identified differentially expressed genes (DEGs) that, when integrated with regulatory elements microRNAs, transcription factors, receptors, and the gut microbiome highlight disease-specific molecular interactions. To assess the diagnostic potential of these molecular signatures, a two-step analysis involving principal component analysis (PCA) and Random Forest classification was conducted, achieving accuracies of 75% for ALF and 89% for NAFLD. Additionally, machine learning algorithms, including K-neighbors, multi-layer perceptron (MLP), decision tree, Random Forest, logistic regression, gradient boosting, CatBoost, Extreme Gradient Boosting (XGB), and Light Gradient Boosting Machine (LGBM), were applied to gene expression data for ALF and NAFLD.

Results

Key genes including CLDN14, EGFR, GSK3B, MYC, and TJP2, alongside regulatory miRNAs let-7a-5p, miR-124-3p, and miR-195-5p and transcription factors NFKB1 and SP1 may be suggested as critical to liver disease progression. Additionally, gut microbiota members, Dictyostelium discoideum and Eikenella might be novel candidates associated with liver disease, highlighting the importance of the gut-liver axis. The Random Forest model reached 75% accuracy and 83% area under the curve for ALF, while NAFLD classification achieved 100% accuracy, precision, recall, and area under the curve underscoring robust diagnostic potential.

Conclusion

This study establishes a solid foundation for further research and therapeutic advancement by identifying key biomolecules and pathways critical to liver disease. Additional experimental validation is needed to confirm clinical applicability.

Noncoding RNA-associated competing endogenous RNA networks in trastuzumab-induced cardiotoxicity

Trastuzumab-induced cardiotoxicity (TIC) is a common and serious disease with abnormal cardiac function. Accumulating evidence has indicated certain non-coding RNAs (ncRNAs), functioning as competing endogenous RNAs (ceRNAs), impacting the progression of cardiovascular diseases. Nonetheless, the specific involvement of ncRNA-mediated ceRNA regulatory mechanisms in TIC remains elusive. The present research aims to comprehensively investigate changes in the expressions of all ncRNA using whole-transcriptome RNA sequencing. The sequencing analysis unveiled significant dysregulation, identifying a total of 43 circular RNAs (circRNAs), 270 long noncoding RNAs (lncRNAs), 12 microRNAs (miRNAs), and 4131 mRNAs in trastuzumab-treated mouse hearts. Subsequently, circRNA-based ceRNA networks consisting of 82 nodes and 91 edges, as well as lncRNA-based ceRNA networks comprising 111 nodes and 112 edges, were constructed. Using the CytoNCA plugin, pivotal genes-miR-31-5p and miR-644-5p-were identified within these networks, exhibiting potential relevance in TIC treatment. Additionally, KEGG and GO analyses were conducted to explore the functional pathways associated with the genes within the ceRNA networks. The outcomes of the predicted ceRNAs and bioinformatics analyses elucidated the plausible involvement of ncRNAs in TIC pathogenesis. This insight contributes to a better understanding of underlying mechanisms and aids in identifying promising targets for effective prevention and treatment strategies.

Integrated bioinformatics combined with machine learning to analyze shared biomarkers and pathways in psoriasis and cervical squamous cell carcinoma

BACKGROUND

Psoriasis extends beyond its dermatological inflammatory manifestations, encompassing systemic inflammation. Existing studies have indicated a potential risk of cervical cancer among patients with psoriasis, suggesting a potential mechanism of co-morbidity. This study aims to explore the key genes, pathways, and immune cells that may link psoriasis and cervical squamous cell carcinoma (CESC).

METHODS

The cervical squamous cell carcinoma dataset (GSE63514) was downloaded from the Gene Expression Omnibus (GEO). Two psoriasis-related datasets (GSE13355 and GSE14905) were merged into one comprehensive dataset after removing batch effects. Differentially expressed genes were identified using Limma and co-expression network analysis (WGCNA), and machine learning random forest algorithm (RF) was used to screen the hub genes. We analyzed relevant gene enrichment pathways using GO and KEGG, and immune cell infiltration in psoriasis and CESC samples using CIBERSORT. The miRNA-mRNA and TFs-mRNA regulatory networks were then constructed using Cytoscape, and the biomarkers for psoriasis and CESC were determined. Potential drug targets were obtained from the cMAP database, and biomarker expression levels in hela and psoriatic cell models were quantified by RT-qPCR.

RESULTS

In this study, we identified 27 key genes associated with psoriasis and cervical squamous cell carcinoma. NCAPH, UHRF1, CDCA2, CENPN and MELK were identified as hub genes using the Random Forest machine learning algorithm. Chromosome mitotic region segregation, nucleotide binding and DNA methylation are the major enrichment pathways for common DEGs in the mitotic cell cycle. Then we analyzed immune cell infiltration in psoriasis and cervical squamous cell carcinoma samples using CIBERSORT. Meanwhile, we used the cMAP database to identify ten small molecule compounds that interact with the central gene as drug candidates for treatment. By analyzing miRNA-mRNA and TFs-mRNA regulatory networks, we identified three miRNAs and nine transcription factors closely associated with five key genes and validated their expression in external validation datasets and clinical samples. Finally, we examined the diagnostic effects with ROC curves, and performed experimental validation in hela and psoriatic cell models.

CONCLUSIONS

We identified five biomarkers, NCAPH, UHRF1, CDCA2, CENPN, and MELK, which may play important roles in the common pathogenesis of psoriasis and cervical squamous cell carcinoma, furthermore predict potential therapeutic agents. These findings open up new perspectives for the diagnosis and treatment of psoriasis and squamous cell carcinoma of the cervix.

Non-apoptotic cell death programs in cervical cancer with an emphasis on ferroptosis

BACKGROUND

Cervical cancer, a pernicious gynecological malignancy, causes the mortality of hundreds of thousands of females worldwide. Despite a considerable decline in mortality, the surging incidence rate among younger women has raised serious concerns. Immortality is the most important characteristic of tumor cells, hence the carcinogenesis of cervical cancer cells pivotally requires compromising with cell death mechanisms.

METHODS

The current study comprehensively reviewed the mechanisms of non-apoptotic cell death programs to provide possible disease management strategies.

RESULTS

Comprehensive evidence has stated that focusing on necroptosis, pyroptosis, and autophagy for disease management is associated with significant limitations such as insufficient understanding, contradictory functions, dependence on disease stage, and complexity of intracellular pathways. However, ferroptosis represents a predictable role in cervix carcinogenesis, and ferroptosis-related genes demonstrate a remarkable correlation with patient survival and clinical outcomes.

CONCLUSION

Ferroptosis may be an appropriate option for disease management strategies from predicting prognosis to treatment.

A 19-Gene Signature of Serous Ovarian Cancer Identified by Machine Learning and Systems Biology: Prospects for Diagnostics and Personalized Medicine

Ovarian cancer is a major cause of cancer deaths among women. Early diagnosis and precision/personalized medicine are essential to reduce mortality and morbidity of ovarian cancer, as with new molecular targets to accelerate drug discovery. We report here an integrated systems biology and machine learning (ML) approach based on the differential coexpression analysis to identify candidate systems biomarkers (i.e., gene modules) for serous ovarian cancer. Accordingly, four independent transcriptome datasets were statistically analyzed independently and common differentially expressed genes (DEGs) were identified. Using these DEGs, coexpressed gene pairs were unraveled. Subsequently, differential coexpression networks between the coexpressed gene pairs were reconstructed so as to identify the differentially coexpressed gene modules. Based on the established criteria, "SOV-module" was identified as being significant, consisting of 19 genes. Using independent datasets, the diagnostic capacity of the SOV-module was evaluated using principal component analysis (PCA) and ML techniques. PCA showed a sensitivity and specificity of 96.7% and 100%, respectively, and ML analysis showed an accuracy of up to 100% in distinguishing phenotypes in the present study sample. The prognostic capacity of the SOV-module was evaluated using survival and ML analyses. We found that the SOV-module's performance for prognostics was significant (p-value = 1.36 × 10-4) with an accuracy of 63% in discriminating between survival and death using ML techniques. In summary, the reported genomic systems biomarker candidate offers promise for personalized medicine in diagnosis and prognosis of serous ovarian cancer and warrants further experimental and translational clinical studies.

Network medicine approaches for identification of novel prognostic systems biomarkers and drug candidates for papillary thyroid carcinoma

Papillary thyroid carcinoma (PTC) is one of the most common endocrine carcinomas worldwide and the aetiology of this cancer is still not well understood. Therefore, it remains important to understand the disease mechanism and find prognostic biomarkers and/or drug candidates for PTC. Compared with approaches based on single-gene assessment, network medicine analysis offers great promise to address this need. Accordingly, in the present study, we performed differential co-expressed network analysis using five transcriptome datasets in patients with PTC and healthy controls. Following meta-analysis of the transcriptome datasets, we uncovered common differentially expressed genes (DEGs) for PTC and, using these genes as proxies, found a highly clustered differentially expressed co-expressed module: a 'PTC-module'. Using independent data, we demonstrated the high prognostic capacity of the PTC-module and designated this module as a prognostic systems biomarker. In addition, using the nodes of the PTC-module, we performed drug repurposing and text mining analyzes to identify novel drug candidates for the disease. We performed molecular docking simulations, and identified: 4-demethoxydaunorubicin hydrochloride, AS605240, BRD-A60245366, ER 27319 maleate, sinensetin, and TWS119 as novel drug candidates whose efficacy was also confirmed by in silico analyzes. Consequently, we have highlighted here the need for differential co-expression analysis to gain a systems-level understanding of a complex disease, and we provide candidate prognostic systems biomarker and novel drugs for PTC.

Immune cell-specific and common molecular signatures in rheumatoid arthritis through molecular network approaches

Rheumatoid arthritis (RA) is an autoimmune disorder and common symptom of RA is chronic synovial inflammation. The pathogenesis of RA is not fully understood. Therefore, we aimed to identify underlying common and distinct molecular signatures and pathways among ten types of tissue and cells obtained from patients with RA. In this study, transcriptomic data including synovial tissues, macrophages, blood, T cells, CD4+T cells, CD8+T cells, natural killer T (NKT), cells natural killer (NK) cells, neutrophils, and monocyte cells were analyzed with an integrative and comparative network biology perspective. Each dataset yielded a list of differentially expressed genes as well as a reconstruction of the tissue-specific protein-protein interaction (PPI) network. Molecular signatures were identified by a statistical test using the hypergeometric probability density function by employing the interactions of transcriptional regulators and PPI. Reporter metabolites of each dataset were determined by using genome-scale metabolic networks. It was defined as the common hub proteins, novel molecular signatures, and metabolites in two or more tissue types while immune cell-specific molecular signatures were identified, too. Importantly, miR-155-5p is found as a common miRNA in all tissues. Moreover, NCOA3, PRKDC and miR-3160 might be novel molecular signatures for RA. Our results establish a novel approach for identifying immune cell-specific molecular signatures of RA and provide insights into the role of common tissue-specific genes, miRNAs, TFs, receptors, and reporter metabolites. Experimental research should be used to validate the corresponding genes, miRNAs, and metabolites.

Differential co-expression network analysis elucidated genes associated with sensitivity to farnesyltransferase inhibitor and prognosis of acute myeloid leukemia

Acute myeloid leukemia (AML) is a heterogeneous disease and the most common form of acute leukemia with a poor prognosis. Due to its complexity, the disease requires the identification of biomarkers for reliable prognosis. To identify potential disease genes that regulate patient prognosis, we used differential co-expression network analysis and transcriptomics data from relapsed, refractory, and previously untreated AML patients based on their response to treatment in the present study. In addition, we combined functional genomics and transcriptomics data to identify novel and therapeutically potential systems biomarkers for patients who do or do not respond to treatment. As a result, we constructed co-expression networks for response and non-response cases and identified a highly interconnected group of genes consisting of SECISBP2L, MAN1A2, PRPF31, VASP, and SNAPC1 in the response network and a group consisting of PHTF2, SLC11A2, PDLIM5, OTUB1, and KLRD1 in the non-response network, both of which showed high prognostic performance with hazard ratios of 4.12 and 3.66, respectively. Remarkably, ETS1, GATA2, AR, YBX1, and FOXP3 were found to be important transcription factors in both networks. The prognostic indicators reported here could be considered as a resource for identifying tumorigenesis and chemoresistance to farnesyltransferase inhibitor. They could help identify important research directions for the development of new prognostic and therapeutic techniques for AML.

Bioinformatics and cheminformatics approaches to identify pathways, molecular mechanisms and drug substances related to genetic basis of cervical cancer

Cervical cancer (CC) is a global threat to women and our knowledge is frighteningly little about its underlying genomic contributors. Our research aimed to understand the underlying molecular and genetic mechanisms of CC by integrating bioinformatics and network-based study. Transcriptomic analyses of three microarray datasets identified 218 common differentially expressed genes (DEGs) within control samples and CC specimens. KEGG pathway analysis revealed pathways in cell cycle, drug metabolism, DNA replication and the significant GO terms were cornification, proteolysis, cell division and DNA replication. Protein-protein interaction (PPI) network analysis identified 20 hub genes and survival analyses validated CDC45, MCM2, PCNA and TOP2A as CC biomarkers. Subsequently, 10 transcriptional factors (TFs) and 10 post-transcriptional regulators were detected through TFs-DEGs and miRNAs-DEGs regulatory network assessment. Finally, the CC biomarkers were subjected to a drug-gene relationship analysis to find the best target inhibitors. Standard cheminformatics method including in silico ADMET and molecular docking study substantiated PD0325901 and Selumetinib as the most potent candidate-drug for CC treatment. Overall, this meticulous study holds promises for further in vitro and in vivo research on CC diagnosis, prognosis and therapies. Communicated by Ramaswamy H. Sarma.

Bioinformatics Prediction and Machine Learning on Gene Expression Data Identifies Novel Gene Candidates in Gastric Cancer

Gastric cancer (GC) is one of the five most common cancers in the world and unfortunately has a high mortality rate. To date, the pathogenesis and disease genes of GC are unclear, so the need for new diagnostic and prognostic strategies for GC is undeniable. Despite particular findings in this regard, a holistic approach encompassing molecular data from different biological levels for GC has been lacking. To translate Big Data into system-level biomarkers, in this study, we integrated three different GC gene expression data with three different biological networks for the first time and captured biologically significant (i.e., reporter) transcripts, hub proteins, transcription factors, and receptor molecules of GC. We analyzed the revealed biomolecules with independent RNA-seq data for their diagnostic and prognostic capabilities. While this holistic approach uncovered biomolecules already associated with GC, it also revealed novel system biomarker candidates for GC. Classification performances of novel candidate biomarkers with machine learning approaches were investigated. With this study, AES, CEBPZ, GRK6, HPGDS, SKIL, and SP3 were identified for the first time as diagnostic and/or prognostic biomarker candidates for GC. Consequently, we have provided valuable data for further experimental and clinical efforts that may be useful for the diagnosis and/or prognosis of GC.

Gene co-expression network analysis revealed novel biomarkers for ovarian cancer

Ovarian cancer is the second most common gynecologic cancer and remains the leading cause of death of all gynecologic oncologic disease. Therefore, understanding the molecular mechanisms underlying the disease, and the identification of effective and predictive biomarkers are invaluable for the development of diagnostic and treatment strategies. In the present study, a differential co-expression network analysis was performed via meta-analysis of three transcriptome datasets of serous ovarian adenocarcinoma to identify novel candidate biomarker signatures, i.e. genes and miRNAs. We identified 439 common differentially expressed genes (DEGs), and reconstructed differential co-expression networks using common DEGs and considering two conditions, i.e. healthy ovarian surface epithelia samples and serous ovarian adenocarcinoma epithelia samples. The modular analyses of the constructed networks indicated a co-expressed gene module consisting of 17 genes. A total of 11 biomarker candidates were determined through receiver operating characteristic (ROC) curves of gene expression of module genes, and miRNAs targeting these genes were identified. As a result, six genes (CDT1, CNIH4, CRLS1, LIMCH1, POC1A, and SNX13), and two miRNAs (mir-147a, and mir-103a-3p) were suggested as novel candidate prognostic biomarkers for ovarian cancer. Further experimental and clinical validation of the proposed biomarkers could help future development of potential diagnostic and therapeutic innovations in ovarian cancer.

LncRNA TP53TG1 plays an anti-oncogenic role in cervical cancer by synthetically regulating transcriptome profile in HeLa cells

Long non-coding RNAs (lncRNAs) have been extensively studied as important regulators of tumor development in various cancers. Tumor protein 53 target gene 1 (TP53TG1) is a newly identified lncRNA in recent years, and several studies have shown that TP53TG1 may play oncogenic or anti-oncogenic roles in different cancers. Nevertheless, the role of TP53TG1 in the development of cervical cancer is unclear. In our study, pan-cancer analysis showed that high expression of TP53TG1 was significantly associated with a better prognosis. We then constructed a TP53TG1 overexpression model in HeLa cell line to explore its functions and molecular targets. We found that TP53TG1 overexpression significantly inhibited cell proliferation and induced apoptosis, demonstrating that TP53TG1 may be a novel anti-oncogenic factor in cervical cancer. Furthermore, overexpression of TP53TG1 could activate type I interferon signaling pathways and inhibit the expression of genes involved in DNA damage responses. Meanwhile, TP53TG1 could affect alternative splicing of genes involved in cell proliferation or apoptosis by regulating the expression of many RNA-binding protein genes. Competing endogenous RNA (ceRNA) network analysis demonstrated that TP53TG1 could act as the sponge of several miRNAs to regulate the expression level of target genes. In conclusion, our study highlights the essential role of lncRNA TP53TG1 in the development of cervical cancer and suggests the potential regulatory mechanisms.

Exploration of biomarkers for the diagnosis, treatment and prognosis of cervical cancer: a review

As the fourth most diagnosed cancer, cervical cancer (CC) is one of the major causes of cancer-related mortality affecting females globally, particularly when diagnosed at advanced stage. Discoveries of CC biomarkers pave the road to precision medicine for better patient outcomes. High throughput omics technologies, characterized by big data production further accelerate the process. To date, various CC biomarkers have been discovered through the advancement in technologies. Despite, very few have successfully translated into clinical practice due to the paucity of validation through large scale clinical studies. While vast amounts of data are generated by the omics technologies, challenges arise in identifying the clinically relevant data for translational research as analyses of single-level omics approaches rarely provide causal relations. Integrative multi-omics approaches across different levels of cellular function enable better comprehension of the fundamental biology of CC by highlighting the interrelationships of the involved biomolecules and their function, aiding in identification of novel integrated biomarker profile for precision medicine. Establishment of a worldwide Early Detection Research Network (EDRN) system helps accelerating the pace of biomarker translation. To fill the research gap, we review the recent research progress on CC biomarker development from the application of high throughput omics technologies with sections covering genomics, transcriptomics, proteomics, and metabolomics.

Repositioning of Anti-Inflammatory Drugs for the Treatment of Cervical Cancer Sub-Types

Cervical cancer is the fourth most commonly diagnosed cancer worldwide and, in almost all cases is caused by infection with highly oncogenic Human Papillomaviruses (HPVs). On the other hand, inflammation is one of the hallmarks of cancer research. Here, we focused on inflammatory proteins that classify cervical cancer patients by considering individual differences between cancer patients in contrast to conventional treatments. We repurposed anti-inflammatory drugs for therapy of HPV-16 and HPV-18 infected groups, separately. In this study, we employed systems biology approaches to unveil the diagnostic and treatment options from a precision medicine perspective by delineating differential inflammation-associated biomarkers associated with carcinogenesis for both subtypes. We performed a meta-analysis of cervical cancer-associated transcriptomic datasets considering subtype differences of samples and identified the differentially expressed genes (DEGs). Using gene signature reversal on HPV-16 and HPV-18, we performed both signature- and network-based drug reversal to identify anti-inflammatory drug candidates against inflammation-associated nodes. The anti-inflammatory drug candidates were evaluated using molecular docking to determine the potential of physical interactions between the anti-inflammatory drug and inflammation-associated nodes as drug targets. We proposed 4 novels anti-inflammatory drugs (AS-601245, betamethasone, narciclasin, and methylprednisolone) for the treatment of HPV-16, 3 novel drugs for the treatment of HPV-18 (daphnetin, phenylbutazone, and tiaprofenoic acid), and 5 novel drugs (aldosterone, BMS-345541, etodolac, hydrocortisone, and prednisolone) for the treatment of both subtypes. We proposed anti-inflammatory drug candidates that have the potential to be therapeutic agents for the prevention and/or treatment of cervical cancer.

Multiomics Data Integration Identifies New Molecular Signatures for Abdominal Aortic Aneurysm and Aortic Occlusive Disease: Implications for Early Diagnosis, Prognosis, and Therapeutic Targets

Cardiovascular disease (CVD) is the leading cause of death among adults in developed countries. Among CVDs, abdominal aortic aneurysm (AAA) and aortic occlusive disease (AOD) are of great public health importance because of the high mortality rate in the elderly population. Despite significant molecular insights into AAA and AOD, the molecular mechanisms of these diseases remain unclear, and the current lack of robust diagnostic and prognostic biomarkers requires novel approaches to biomarker discovery and molecular targeting. In this study, we performed a comparative analysis of genome-wide expression data from patients with large AAA (n = 29), small AAA (n = 20), AOD (n = 9), and controls (n = 10). Specifically, we identified the differentially expressed genes and associated molecular pathways and biological processes (BPs) in each disease. Using a systems science approach, these data were linked to comprehensive human biological networks (i.e., protein-protein interaction, transcriptional regulatory, and metabolic networks) to identify molecular signatures of the salient mechanisms of AAA and AOD. Significant alterations in lipid metabolism and valine, leucine, and isoleucine metabolism, as well as neurodegenerative diseases and sex differences in the pathogenesis of AAA and AOD were identified. In the presence of aneurysm, size-dependent changes in lipid metabolism were observed. In addition, molecules and signaling pathways related to immunity, inflammation, infectious disease, and oxidative phosphorylation were identified in common. The results of the comparative and integrative analyzes revealed important clues to disease mechanisms and reporter molecules at various levels that warrant future development as potential prognostic biomarkers and putative therapeutic targets.

In the Tumor Microenvironment, ETS1 Is an Oncogenic Immune Protein: An Integrative Pancancer Analysis

Background

Previous research suggested that ETS1 (ETS proto-oncogene 1, transcription factor) could be useful for cancer immunotherapy. The processes underlying its therapeutic potential, on the other hand, have yet to be thoroughly investigated. The purpose of this study was to look into the relationship between ETS1 expression and immunity.

Methods

TCGA and GEO provide raw data on 33 different cancers as well as GSE67501, GSE78220, and IMvigor210. In addition, we looked at ETS1's genetic changes, expression patterns, and survival studies. The linkages between ETS1 and TME, as well as its association with immunological processes/elements and the major histocompatibility complex, were explored to effectively understand the role of ETS1 in cancer immunotherapy. Three distinct immunotherapeutic cohorts were employed to examine the relationship between ETS1 and immunotherapeutic response.

Results

ETS1 expression was shown to be high in tumor tissue. ETS1 overexpression is linked to a worse clinical outcome in individuals with overall survival. Immune cell infiltration, immunological modulators, and immunotherapeutic signs are all linked to ETS1. Overexpression of ETS1 is linked to immune-related pathways. However, no statistically significant link was found between ETS1 and immunotherapeutic response.

Conclusions

ETS1 may be a reliable biomarker for tumor prognosis and a viable prospective therapeutic target for human cancer immunotherapy (e.g., KIRP, MESO, BLCA, KIRC, and THYM).

Integrative Analysis for Identification of Therapeutic Targets and Prognostic Signatures in Non-Small Cell Lung Cancer

Differential expressions of certain genes during tumorigenesis may serve to identify novel manageable targets in the clinic. In this work with an integrated bioinformatics approach, we analyzed public microarray datasets from Gene Expression Omnibus (GEO) to explore the key differentially expressed genes (DEGs) in non-small cell lung cancer (NSCLC). We identified a total of 984 common DEGs in 252 healthy and 254 NSCLC gene expression samples. The top 10 DEGs as a result of pathway enrichment and protein–protein interaction analysis were further investigated for their prognostic performances. Among these, we identified high expressions of CDC20, AURKA, CDK1, EZH2, and CDKN2A genes that were associated with significantly poorer overall survival in NSCLC patients. On the contrary, high mRNA expressions of CBL, FYN, LRKK2, and SOCS2 were associated with a significantly better prognosis. Furthermore, our drug target analysis for these hub genes suggests a potential use of Trichostatin A, Pracinostat, TGX-221, PHA-793887, AG-879, and IMD0354 antineoplastic agents to reverse the expression of these DEGs in NSCLC patients.

Systems biomarkers for papillary thyroid cancer prognosis and treatment through multi-omics networks

The identification of biomolecules associated with papillary thyroid cancer (PTC) has upmost importance for the elucidation of the disease mechanism and the development of effective diagnostic and treatment strategies. Despite particular findings in this regard, a holistic analysis encompassing molecular data from different biological levels has been lacking. In the present study, a meta-analysis of four transcriptome datasets was performed to identify gene expression signatures in PTC, and reporter molecules were determined by mapping gene expression data onto three major cellular networks, i.e., transcriptional regulatory, protein-protein interaction, and metabolic networks. We identified 282 common genes that were differentially expressed in all PTC datasets. In addition, six proteins (FYN, JUN, LYN, PML, SIN3A, and RARA), two Erb-B2 receptors (ERBB2 and ERBB4), two cyclin-dependent receptors (CDK1 and CDK2), and three histone deacetylase receptors (HDAC1, HDAC2, and HDAC3) came into prominence as proteomic signatures in addition to several metabolites including lactaldehyde and proline at the metabolome level. Significant associations with calcium and MAPK signaling pathways and transcriptional and post-transcriptional activities of 12 TFs and 110 miRNAs were also observed at the regulatory level. Among them, six miRNAs (miR-30b-3p, miR-15b-5p, let-7a-5p, miR-130b-3p, miR-424-5p, and miR-193b-3p) were associated with PTC for the first time in the literature, and the expression levels of miR-30b-3p, miR-15b-5p, and let-7a-5p were found to be predictive of disease prognosis. Drug repositioning and molecular docking simulations revealed that 5 drugs (prochlorperazine, meclizine, rottlerin, cephaeline, and tretinoin) may be useful in the treatment of PTC. Consequently, we report here biomolecule candidates that may be considered as prognostic biomarkers or potential therapeutic targets for further experimental and clinical trials for PTC.

Forecasting Gastric Cancer Diagnosis, Prognosis, and Drug Repurposing with Novel Gene Expression Signatures

Gastric cancer (GC) is a prevalent disease worldwide with high mortality and poor treatment success. Early diagnosis of GC and forecasting of its prognosis with the use of biomarkers are directly relevant to achieve both personalized/precision medicine and innovation in cancer therapeutics. Gene expression signatures offer one of the promising avenues of research in this regard, as well as guiding drug repurposing analyses in cancers. Using publicly accessible gene expression datasets from the Gene Expression Omnibus and The Cancer Genome Atlas (TCGA), we report here original findings on co-expressed gene modules that are differentially expressed between 133 GC samples and 46 normal tissues, and thus hold potential for novel diagnostic candidates for GC. Furthermore, we found two co-expressed gene modules were significantly associated with poor survival outcomes revealed by survival analysis of the RNA-Seq TCGA datasets. We identified STAT6 (signal transducer and activator of transcription 6) as a key regulator of the identified gene modules. Finally, potential therapeutic drugs that may target and reverse the expression of the identified altered gene modules examined for drug repurposing analyses and the unraveled compounds were further investigated in the literature by the text mining method. Accordingly, we found several repurposed drug candidates, including Trichostatin A, Vorinostat, Parthenolide, Panobinostat, Brefeldin A, Belinostat, and Danusertib. Through text mining analysis and literature search validation, Belinostat and Danusertib were suggested as possible novel drug candidates for GC treatment. These findings collectively inform multiple aspects of GC medical management, including its precision diagnosis, forecasting of possible outcomes, and drug repurposing for innovation in GC medicines in the future.

Data analysis methods for defining biomarkers from omics data

Omics mainly includes genomics, epigenomics, transcriptomics, proteomics and metabolomics. The rapid development of omics technology has opened up new ways to study disease diagnosis and prognosis and to define prospective information of complex diseases. Since omics data are usually large and complex, the method used to analyze the data and to define important information is crucial in omics study. In this review, we focus on advances in biomarker discovery methods based on omics data in the last decade, and categorize them as individual feature analysis, combinatorial feature analysis and network analysis. We also discuss the challenges and perspectives in this field.

Prioritizing the candidate genes related to cervical cancer using the moment of inertia tensor

It is well known that cervical cancer poses the fourth most malignancy threat to women worldwide among all cancer types. There is a tremendous improvement in realizing the underlying molecular associations in cervical cancer. Several studies reported pieces of evidence for the involvement of various genes in the disease progression. However, with the ever-evolving bioinformatics tools, there has been an upsurge in predicting numerous genes responsible for cervical cancer progression and making it highly complex to target the genes for further evaluation. In this article, we prioritized the candidate genes based on the sequence similarity analysis with known cancer genes. For this purpose, we used the concept of the moment of inertia tensor, which reveals the similarities between the protein sequences more efficiently. Tensor for moment of inertia explores the similarity of the protein sequences based on the physicochemical properties of amino acids. From our analysis, we obtained 14 candidate cervical cancer genes, which are highly similar to known cervical cancer genes. Further, we analyzed the GO terms and prioritized these genes based on the number of hits with biological process, molecular functions, and their involvement in KEGG pathways. We also discussed the evidence-based involvement of the prioritized genes in other cancers and listed the available drugs for those genes.

Cancer Stem Cell Transcriptome Profiling Reveals Seed Genes of Tumorigenesis: New Avenues for Cancer Precision Medicine

Cancer stem-like cells (CSCs) possess the ability to self-renew and differentiate, and they are among the major factors driving tumorigenesis, metastasis, and resistance to chemotherapy. Therefore, it is critical to understand the molecular substrates of CSC biology so as to discover novel molecular biosignatures that distinguish CSCs and tumor cells. Here, we report new findings and insights by employing four transcriptome datasets associated with CSCs, with CSC and tumor samples from breast, lung, oral, and ovarian tissues. The CSC samples were analyzed to identify differentially expressed genes between CSC and tumor phenotypes. Through comparative profiling of expression levels in different cancer types, we identified 17 "seed genes" that showed a mutual differential expression pattern. We showed that these seed genes were strongly associated with cancer-associated signaling pathways and biological processes, the immune system, and the key cancer hallmarks. Further, the seed genes presented significant changes in their expression profiles in different cancer types and diverse mutation rates, and they also demonstrated high potential as diagnostic and prognostic biomarkers in various cancers. We report a number of seed genes that represent significant potential as "systems biomarkers" for understanding the pathobiology of tumorigenesis. Seed genes offer a new innovation avenue for potential applications toward cancer precision medicine in a broad range of cancers in oncology in the future.

The Clinical Significance and Biological Function of PCDH7 in Cervical Cancer

Purpose

Cervical cancer is a common cancerous tumor in women that is prone to recurrence and metastasis. Recently, many people have explored the role of protocadherin 7 (PCDH7) in cancer and found that PCDH7 is abnormally expressed in many cancers. The purpose of this study is to explore the expression and mechanism of PCDH7 in cervical cancer and evaluate its clinical prognostic significance.

Materials and Methods

The expression of PCDH7 in cervical cancer and cells was measured by qRT-PCR. The relationship between PCDH7 expression and the clinical prognosis was calculated using the Kaplan–Meier method and Cox regression analyses. Effects of PCDH7 on cancer cell proliferation, migration, and invasion were studied by MTT assay and transwell assays.

Results

The expression of PCDH7 in cervical cancer tissues and cell lines was notably downregulated compared with the corresponding control. Low PCDH7 expression was associated with a low survival rate. PCDH7 expression was correlated with lymph node metastasis, cell differentiation, and FIGO staging. PCDH7 can be used as an independent prognostic factor for cervical cancer. Up-regulation of PCDH7 significantly inhibited the proliferation ability, migration potential, and invasion capacity of cancer cells.

Conclusion

PCDH7 may be used as a prognostic biomarker for cervical cancer patients.

Drug Repositioning for P-Glycoprotein Mediated Co-Expression Networks in Colorectal Cancer

Colorectal cancer (CRC) is one of the most fatal types of cancers that is seen in both men and women. CRC is the third most common type of cancer worldwide. Over the years, several drugs are developed for the treatment of CRC; however, patients with advanced CRC can be resistant to some drugs. P-glycoprotein (P-gp) (also known as Multidrug Resistance 1, MDR1) is a well-identified membrane transporter protein expressed by ABCB1 gene. The high expression of MDR1 protein found in several cancer types causes chemotherapy failure owing to efflux drug molecules out of the cancer cell, decreases the drug concentration, and causes drug resistance. As same as other cancers, drug-resistant CRC is one of the major obstacles for effective therapy and novel therapeutic strategies are urgently needed. Network-based approaches can be used to determine specific biomarkers, potential drug targets, or repurposing approved drugs in drug-resistant cancers. Drug repositioning is the approach for using existing drugs for a new therapeutic purpose; it is a highly efficient and low-cost process. To improve current understanding of the MDR-1-related drug resistance in CRC, we explored gene co-expression networks around ABCB1 gene with different network sizes (50, 100, 150, 200 edges) and repurposed candidate drugs targeting the ABCB1 gene and its co-expression network by using drug repositioning approach for the treatment of CRC. The candidate drugs were also assessed by using molecular docking for determining the potential of physical interactions between the drug and MDR1 protein as a drug target. We also evaluated these four networks whether they are diagnostic or prognostic features in CRC besides biological function determined by functional enrichment analysis. Lastly, differentially expressed genes of drug-resistant (i.e., oxaliplatin, methotrexate, SN38) HT29 cell lines were found and used for repurposing drugs with reversal gene expressions. As a result, it is shown that all networks exhibited high diagnostic and prognostic performance besides the identification of various drug candidates for drug-resistant patients with CRC. All these results can shed light on the development of effective diagnosis, prognosis, and treatment strategies for drug resistance in CRC.

Bioinformatics analysis shows that TOP2A functions as a key candidate gene in the progression of cervical cancer

Cervical cancer (CC) is one of the most prevalent types of cancer affecting females worldwide. However, the molecular mechanisms underlying the development and progression of CC remains to be elucidated. Taking the high incidence and mortality rates amongst women into consideration, the identification of novel biomarkers to prevent CC is of great significance and required to improve diagnosis. Using three raw microarray datasets from the Gene Expression Omnibus database, 188 differentially expressed genes (DEGs) were identified. Gene Ontology and pathway analyses were performed on the DEGs. Through protein-protein interaction network construction and module analysis, eight hub genes [cell division cycle 6, cyclin-dependent kinase 1 (CDK1), cell division control protein 45, budding uninhibited by benzimidazoles 1 (BUB1), DNA topoisomerase II α (TOP2A) and minichromosome maintenance complex component 4, CCNB2 and CCNB1] were identified, but only TOP2A was considered a prognostic factor in survival analysis. There were strong positive correlations between TOP2A and BUB1 (P<0.0001, rs=0.635), CDK1 (P<0.0001, rs=0.511), centromere protein F (CENPF) (P<0.0001, rs=0.677), Rac GTPase activating protein 1 (RACGAP1) (P<0.0001, rs=0.612), F-box protein 5 (FBXO5) (P<0.0001, rs=0.585) and BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B) (P<0.0001, rs=0.584). Additionally, BUB1, CDK1, CENPF, RACGAP1, FBXO5 and BUB1B are all potentially suitable candidate targets for the diagnosis and treatment of CC. In conclusion, the present study identified TOP2A as a potential tumor oncogene and a biomarker for the prognosis of CC.

Rett Syndrome, a Neurodevelopmental Disorder, Whole-Transcriptome, and Mitochondrial Genome Multiomics Analyses Identify Novel Variations and Disease Pathways

Rett syndrome (RTT) is a severe neurodevelopmental disorder reported worldwide in diverse populations. RTT is diagnosed primarily in females, with clinical findings manifesting early in life. Despite the variable rates across populations, RTT has an estimated prevalence of ∼1 in 10,000 live female births. Among 215 Saudi Arabian patients with neurodevelopmental and autism spectrum disorders, we identified 33 patients with RTT who were subsequently examined by genome-wide transcriptome and mitochondrial genome variations. To the best of our knowledge, this is the first in-depth molecular and multiomics analyses of a large cohort of Saudi RTT cases with a view to informing the underlying mechanisms of this disease that impact many patients and families worldwide. The patients were unrelated, except for 2 affected sisters, and comprised of 25 classic and eight atypical RTT cases. The cases were screened for methyl-CpG binding protein 2 (MECP2), CDKL5, FOXG1, NTNG1, and mitochondrial DNA (mtDNA) variants, as well as copy number variations (CNVs) using a genome-wide experimental strategy. We found that 15 patients (13 classic and two atypical RTT) have MECP2 mutations, 2 of which were novel variants. Two patients had novel FOXG1 and CDKL5 variants (both atypical RTT). Whole mtDNA sequencing of the patients who were MECP2 negative revealed two novel mtDNA variants in two classic RTT patients. Importantly, the whole-transcriptome analysis of our RTT patients' blood and further comparison with previous expression profiling of brain tissue from patients with RTT revealed 77 significantly dysregulated genes. The gene ontology and interaction network analysis indicated potentially critical roles of MAPK9, NDUFA5, ATR, SMARCA5, RPL23, SRSF3, and mitochondrial dysfunction, oxidative stress response and MAPK signaling pathways in the pathogenesis of RTT genes. This study expands our knowledge on RTT disease networks and pathways as well as presents novel mutations and mtDNA alterations in RTT in a population sample that was not previously studied.

OScc: an online survival analysis web server to evaluate the prognostic value of biomarkers in cervical cancer

Aim: To establish a web server that can mutually validate prognostic biomarkers of cervical cancer. Methods: Four datasets including expression profiling and relative clinical follow-up data were collected from Gene Expression Omnibus and The Cancer Genome Atlas. The web server was developed by R software. Results: The web server was named OScc including 690 patients and can be accessed at http://bioinfo.henu.edu.cn/CESC/CESCList.jsp . The Kaplan–Meier survival curves with log-rank p-value and hazard ratio will be generated of interested gene in OScc. Compared with previous predictive tools, OScc had the advantages of registration-free, larger sample size and subgroup analysis. Conclusion: The OScc is highly valuable to perform the preliminary assessment and validation of new or interested prognostic biomarkers for cervical cancer.

Roles of chitinase 3-like 1 in the development of cancer, neurodegenerative diseases, and inflammatory diseases

Chitinase 3-like 1 (CHI3L1) is a secreted glycoprotein that mediates inflammation, macrophage polarization, apoptosis, and carcinogenesis. The expression of CHI3L1 is strongly increased by various inflammatory and immunological conditions, including rheumatoid arthritis, multiple sclerosis, Alzheimer's disease, and several cancers. However, its physiological and pathophysiological roles in the development of cancer and neurodegenerative and inflammatory diseases remain unclear. Several studies have reported that CHI3L1 promotes cancer proliferation, inflammatory cytokine production, and microglial activation, and that multiple receptors, such as advanced glycation end product, syndecan-1/αVβ3, and IL-13Rα2, are involved. In addition, the pro-inflammatory action of CHI3L1 may be mediated via the protein kinase B and phosphoinositide-3 signaling pathways and responses to various pro-inflammatory cytokines, including tumor necrosis factor-α, interleukin-1β, interleukin-6, and interferon-γ. Therefore, CHI3L1 could contribute to a vast array of inflammatory diseases. In this article, we review recent findings regarding the roles of CHI3L1 and suggest therapeutic approaches targeting CHI3L1 in the development of cancers, neurodegenerative diseases, and inflammatory diseases.