Network-based identification of biomarkers coexpressed with multiple pathways

Pan-cancer analysis for the prognostic and immunological role of CD47: interact with TNFRSF9 inducing CD8 + T cell exhaustion

PURPOSE

The research endeavors to explore the implications of CD47 in cancer immunotherapy effectiveness. Specifically, there is a gap in comprehending the influence of CD47 on the tumor immune microenvironment, particularly in relation to CD8 + T cells. Our study aims to elucidate the prognostic and immunological relevance of CD47 to enhance insights into its prospective utilities in immunotherapeutic interventions.

METHODS

Differential gene expression analysis, prognosis assessment, immunological infiltration evaluation, pathway enrichment analysis, and correlation investigation were performed utilizing a combination of R packages, computational algorithms, diverse datasets, and patient cohorts. Validation of the concept was achieved through the utilization of single-cell sequencing technology.

RESULTS

CD47 demonstrated ubiquitous expression across various cancer types and was notably associated with unfavorable prognostic outcomes in pan-cancer assessments. Immunological investigations unveiled a robust correlation between CD47 expression and T-cell infiltration rather than T-cell exclusion across multiple cancer types. Specifically, the CD47-high group exhibited a poorer prognosis for the cytotoxic CD8 + T cell Top group compared to the CD47-low group, suggesting a potential impairment of CD8 + T cell functionality by CD47. The exploration of mechanism identified enrichment of CD47-associated differentially expressed genes in the CD8 + T cell exhausted pathway in multiple cancer contexts. Further analyses focusing on the CD8 TCR Downstream Pathway and gene correlation patterns underscored the significant involvement of TNFRSF9 in mediating these effects.

CONCLUSION

A robust association exists between CD47 and the exhaustion of CD8 + T cells, potentially enabling immune evasion by cancer cells and thereby contributing to adverse prognostic outcomes. Consequently, genes such as CD47 and those linked to T-cell exhaustion, notably TNFRSF9, present as promising dual antigenic targets, providing critical insights into the field of immunotherapy.

Integrating DNA methylation and gene expression data in a single gene network using the iNETgrate package

Analyzing different omics data types independently is often too restrictive to allow for detection of subtle, but consistent, variations that are coherently supported based upon different assays. Integrating multi-omics data in one model can increase statistical power. However, designing such a model is challenging because different omics are measured at different levels. We developed the iNETgrate package ( https://bioconductor.org/packages/iNETgrate/ ) that efficiently integrates transcriptome and DNA methylation data in a single gene network. Applying iNETgrate on five independent datasets improved prognostication compared to common clinical gold standards and a patient similarity network approach.

Application of Protein-Protein Interaction Network Analysis in Order to Identify Cervical Cancer miRNA and mRNA Biomarkers

Cervical cancer (CC) is one of the world's most common and severe cancers. This cancer includes two histological types: squamous cell carcinoma (SCC) and adenocarcinoma (ADC). The current study aims at identifying novel potential candidate mRNA and miRNA biomarkers for SCC based on a protein-protein interaction (PPI) and miRNA-mRNA network analysis. The current project utilized a transcriptome profile for normal and SCC samples. First, the PPI network was constructed for the 1335 DEGs, and then, a significant gene module was extracted from the PPI network. Next, a list of miRNAs targeting module's genes was collected from the experimentally validated databases, and a miRNA-mRNA regulatory network was formed. After network analysis, four driver genes were selected from the module's genes including MCM2, MCM10, POLA1, and TONSL and introduced as potential candidate biomarkers for SCC. In addition, two hub miRNAs, including miR-193b-3p and miR-615-3p, were selected from the miRNA-mRNA regulatory network and reported as possible candidate biomarkers. In summary, six potential candidate RNA-based biomarkers consist of four genes containing MCM2, MCM10, POLA1, and TONSL, and two miRNAs containing miR-193b-3p and miR-615-3p are opposed as potential candidate biomarkers for CC.

"iNETgrate": integrating DNA methylation and gene expression data in a single gene network

Integrating multi-omics data in one model can increase statistical power. However, designing such a model is challenging because different omics are measured at different levels. We developed the iNETgrate package (https://bioconductor.org/packages/iNETgrate/) that efficiently integrates transcriptome and DNA methylation data in a single gene network. Applying iNETgrate on five independent datasets improved prognostication compared to common clinical gold standards and a patient similarity network approach.

Computational Tactics for Precision Cancer Network Biology

Network biology has garnered tremendous attention in understanding complex systems of cancer, because the mechanisms underlying cancer involve the perturbations in the specific function of molecular networks, rather than a disorder of a single gene. In this article, we review the various computational tactics for gene regulatory network analysis, focused especially on personalized anti-cancer therapy. This paper covers three major topics: (1) cell line's (or patient's) cancer characteristics specific gene regulatory network estimation, which enables us to reveal molecular interplays under varying conditions of cancer characteristics of cell lines (or patient); (2) computational approaches to interpret the multitudinous and massive networks; (3) network-based application to uncover molecular mechanisms of cancer and related marker identification. We expect that this review will help readers understand personalized computational network biology that plays a significant role in precision cancer medicine.

Integrating machine learning to construct aberrant alternative splicing event related classifiers to predict prognosis and immunotherapy response in patients with hepatocellular carcinoma

Introduction: In hepatocellular carcinoma (HCC), alternative splicing (AS) is related to tumor invasion and progression.

Methods: We used HCC data from a public database to identify AS subtypes by unsupervised clustering. Through feature analysis of different splicing subtypes and acquisition of the differential alternative splicing events (DASEs) combined with enrichment analysis, the differences in several subtypes were explored, cell function studies have also demonstrated that it plays an important role in HCC.

Results: Finally, in keeping with the differences between these subtypes, DASEs identified survival-related AS times, and were used to construct risk proportional regression models. AS was found to be useful for the classification of HCC subtypes, which changed the activity of tumor-related pathways through differential splicing effects, affected the tumor microenvironment, and participated in immune reprogramming.

Conclusion: In this study, we described the clinical and molecular characteristics providing a new approach for the personalized treatment of HCC patients.

Network Medicine-Based Analysis of Association Between Gynecological Cancers and Metabolic and Hormonal Disorders

Different metabolic and hormonal disorders like type 2 diabetes mellitus (T2DM), obesity, and polycystic ovary syndrome (PCOS) have tangible socio-economic impact. Prevalence of these metabolic and hormonal disorders is steadily increasing among women. There are clinical evidences that these physiological conditions are related to the manifestation of different gynecological cancers and their poor prognosis. The relationship between metabolic and hormonal disorders with gynecological cancers is quite complex. The need for gene level association study is extremely important to find markers and predicting risk factors. In the current work, we have selected metabolic disorders like T2DM and obesity, hormonal disorder PCOS, and 4 different gynecological cancers like endometrial, uterine, cervical, and triple negative breast cancer (TNBC). The gene list was downloaded from DisGeNET database (v 6.0). The protein interaction network was constructed using HIPPIE (v 2.2) and shared proteins were identified. Molecular comorbidity index and Jaccard coefficient (degree of similarity) between the diseases were determined. Pathway enrichment analysis was done using ReactomePA and significant modules (clusters in a network) of the constructed network was analyzed by MCODE plugin of Cytoscape. The comorbid conditions like PCOS-obesity found to increase the risk factor of ovarian and triple negative breast cancers whereas PCOS alone has highest contribution to the endometrial cancer. Different gynecological cancers were found to be differentially related to the metabolic/hormonal disorders and comorbid condition.

Immune-Omics Networks of CD27, PD1, and PDL1 in Non-Small Cell Lung Cancer

To date, there are no prognostic/predictive biomarkers to select chemotherapy, immunotherapy, and radiotherapy in individual non-small cell lung cancer (NSCLC) patients. Major immune-checkpoint inhibitors (ICIs) have more DNA copy number variations (CNV) than mutations in The Cancer Genome Atlas (TCGA) NSCLC tumors. Nevertheless, CNV-mediated dysregulated gene expression in NSCLC is not well understood. Integrated CNV and transcriptional profiles in NSCLC tumors (n = 371) were analyzed using Boolean implication networks for the identification of a multi-omics CD27, PD1, and PDL1 network, containing novel prognostic genes and proliferation genes. A 5-gene (EIF2AK3, F2RL3, FOSL1, SLC25A26, and SPP1) prognostic model was developed and validated for patient stratification (p < 0.02, Kaplan-Meier analyses) in NSCLC tumors (n = 1163). A total of 13 genes (COPA, CSE1L, EIF2B3, LSM3, MCM5, PMPCB, POLR1B, POLR2F, PSMC3, PSMD11, RPL32, RPS18, and SNRPE) had a significant impact on proliferation in 100% of the NSCLC cell lines in both CRISPR-Cas9 (n = 78) and RNA interference (RNAi) assays (n = 92). Multiple identified genes were associated with chemoresponse and radiotherapy response in NSCLC cell lines (n = 117) and patient tumors (n = 966). Repurposing drugs were discovered based on this immune-omics network to improve NSCLC treatment.

Understanding the Determination of Meat Quality Using Biochemical Characteristics of the Muscle: Stress at Slaughter and Other Missing Keys

Despite increasingly detailed knowledge of the biochemical processes involved in the determination of meat quality traits, robust models, using biochemical characteristics of the muscle to predict future meat quality, lack. The neglecting of various aspects of the model paradigm may explain this. First, preslaughter stress has a major impact on meat quality and varies according to slaughter context and individuals. Yet, it is rarely taken into account in meat quality models. Second, phenotypic similarity does not imply similarity in the underlying biological causes, and several models may be needed to explain a given phenotype. Finally, the implications of the complexity of biological systems are discussed: a homeostatic equilibrium can be reached in countless ways, involving thousands of interacting processes and molecules at different levels of the organism, changing over time and differing between animals. Consequently, even a robust model may explain a significant part, but not all of the variability between individuals.

Integrative Analysis of Breast Cancer Cells Reveals an Epithelial-Mesenchymal Transition Role in Adaptation to Acidic Microenvironment

Early ducts of breast tumors are unequivocally acidic. High rates of glycolysis combined with poor perfusion lead to a congestion of acidic metabolites in the tumor microenvironment, and pre-malignant cells must adapt to this acidosis to thrive. Adaptation to acidosis selects cancer cells that can thrive in harsh conditions and are capable of outgrowing the normal or non-adapted neighbors. This selection is usually accompanied by phenotypic change. Epithelial mesenchymal transition (EMT) is one of the most important switches correlated to malignant tumor cell phenotype and has been shown to be induced by tumor acidosis. New evidence shows that the EMT switch is not a binary system and occurs on a spectrum of transition states. During confirmation of the EMT phenotype, our results demonstrated a partial EMT phenotype in our acid-adapted cell population. Using RNA sequencing and network analysis we found 10 dysregulated network motifs in acid-adapted breast cancer cells playing a role in EMT. Our further integrative analysis of RNA sequencing and SILAC proteomics resulted in recognition of S100B and S100A6 proteins at both the RNA and protein level. Higher expression of S100B and S100A6 was validated in vitro by Immunocytochemistry. We further validated our finding both in vitro and in patients' samples by IHC analysis of Tissue Microarray (TMA). Correlation analysis of S100A6 and LAMP2b as marker of acidosis in each patient from Moffitt TMA approved the acid related role of S100A6 in breast cancer patients. Also, DCIS patients with higher expression of S100A6 showed lower survival compared to lower expression. We propose essential roles of acid adaptation in cancer cells EMT process through S100 proteins such as S100A6 that can be used as therapeutic strategy targeting both acid-adapted and malignant phenotypes.

Topological properties and in vitro identification of essential nodes of the Paclitaxel and Vincristine interactomes in PC-3 cells

Background

Microtubule-targeting agents (MTAs) disrupt microtubule dynamics, thereby inducing apoptosis via mitochondrial pathway activation through the modulation in the expression of the Bcl-2 family.

Methods

To describe topological features of the MTAs networks associated to intrinsic apoptosis induction in p53-null prostate cancer cells, we predicted and compared the interactomes and topological properties of Paclitaxel and Vincristine, and thus, the essential nodes corresponding with the pro- and anti-apoptotic proteins and their kinetics were subjected to experimental analysis in PC-3 cell line.

Results

The essential nodes of the apoptotic pathways, TP53, and CASP3, were identified in both, Paclitaxel and Vincristine networks, but the intrinsic pathway markers BCL2, BAX, and BCL2L1 were identified as hub nodes only in the Paclitaxel network. An in vitro analysis demonstrated an increase in BimEL and the cleaved-caspase-3 proteins in PC-3 cells exposed to both treatments. Immunoprecipitation analysis showed that treatments induced the releasing of Bax from the anti-apoptotic complex with Bcl-2 protein and the role of BimEL as a de-repressor from sequestering complexes, in addition, new protein complexes were identified between BimEL or Bcl-2 and cleaved-caspase-3, contributing data to the Vincristine network for p53-null cells in response to MTAs.

Conclusion

The differences in sensitivities, protein profiles, and protein complex kinetics observed between the drugs confirmed that the selectivity and stimulation of the apoptotic system vary depending on the cell's genotype, the drug used and its exposure period.

Butyrate-Induced In Vitro Colonocyte Differentiation Network Model Identifies ITGB1, SYK, CDKN2A, CHAF1A, and LRP1 as the Prognostic Markers for Colorectal Cancer Recurrence

Numerous mechanisms are believed to contribute to the role of dietary fiber-derived butyrate in the protection against the development of colorectal cancers (CRCs). To identify the most crucial butyrate-regulated genes, we exploited whole genome microarray of HT-29 cells differentiated in vitro by butyrate treatment. Butyrate differentiates HT-29 cells by relaxing the perturbation, caused by mutations of Adenomatous polyposis coli (APC) and TP53 genes, the most frequent mutations observed in CRC. We constructed protein-protein interaction network (PPIN) with the differentially expressed genes after butyrate treatment and extracted the hub genes from the PPIN, which also participated in the APC-TP53 network. The idea behind this approach was that the expression of these hub genes also regulated cell differentiation, and subsequently CRC prognosis by evading the APC-TP53 mutational effect. We used mRNA expression profile of these critical hub genes from seven large CRC cohorts. Logistic Regression showed strong evidence for association of these common hubs with CRC recurrence. In this study, we exploited PPIN to reduce the dimension of microarray biologically and identified five prognostic markers for the CRC recurrence, which were validated across different datasets. Moreover, these five biomarkers we identified increase the predictive value of the TNM staging for CRC recurrence.

Combining Pathway Identification and Breast Cancer Survival Prediction via Screening-Network Methods

Breast cancer is one of the most common invasive tumors causing high mortality among women. It is characterized by high heterogeneity regarding its biological and clinical characteristics. Several high-throughput assays have been used to collect genome-wide information for many patients in large collaborative studies. This knowledge has improved our understanding of its biology and led to new methods of diagnosing and treating the disease. In particular, system biology has become a valid approach to obtain better insights into breast cancer biological mechanisms. A crucial component of current research lies in identifying novel biomarkers that can be predictive for breast cancer patient prognosis on the basis of the molecular signature of the tumor sample. However, the high dimension and low sample size of data greatly increase the difficulty of cancer survival analysis demanding for the development of ad-hoc statistical methods. In this work, we propose novel screening-network methods that predict patient survival outcome by screening key survival-related genes and we assess the capability of the proposed approaches using METABRIC dataset. In particular, we first identify a subset of genes by using variable screening techniques on gene expression data. Then, we perform Cox regression analysis by incorporating network information associated with the selected subset of genes. The novelty of this work consists in the improved prediction of survival responses due to the different types of screenings (i.e., a biomedical-driven, data-driven and a combination of the two) before building the network-penalized model. Indeed, the combination of the two screening approaches allows us to use the available biological knowledge on breast cancer and complement it with additional information emerging from the data used for the analysis. Moreover, we also illustrate how to extend the proposed approaches to integrate an additional omic layer, such as copy number aberrations, and we show that such strategies can further improve our prediction capabilities. In conclusion, our approaches allow to discriminate patients in high-and low-risk groups using few potential biomarkers and therefore, can help clinicians to provide more precise prognoses and to facilitate the subsequent clinical management of patients at risk of disease.

Sparse Supervised Classification Methods Predict and Characterize Nanomaterial Exposures: Independent Markers of MWCNT Exposures

Recent experimental evidence indicates significant pulmonary toxicity of multiwalled carbon nanotubes (MWCNTs), such as inflammation, interstitial fibrosis, granuloma formation, and carcinogenicity. Although numerous studies explored the adverse potential of various CNTs, their comparability is often limited. This is due to differences in administered dose, physicochemical characteristics, exposure methods, and end points monitored. Here, we addressed the problem through sparse classification method, a supervised machine learning approach that can reduce the noise contained in redundant variables for discriminating among MWCNT-exposed and MWCNT-unexposed groups. A panel of proteins measured from bronchoalveolar lavage fluid (BAL) samples was used to predict exposure to various MWCNT and determine markers that are attributable to MWCNT exposure and toxicity in mice. Using sparse support vector machine-based classification technique, we identified a small subset of proteins clearly distinguishing each exposure. Macrophage-derived chemokine (MDC/CCL22), in particular, was associated with various MWCNT exposures and was independent of exposure method employed, that is, oropharyngeal aspiration versus inhalation exposure. Sustained expression of some of the selected protein markers identified also suggests their potential role in MWCNT-induced toxicity and proposes hypotheses for future mechanistic studies. Such approaches can be used more broadly for nanomaterial risk profiling studies to evaluate decisions related to dose/time-response relationships that could delineate experimental variables from exposure markers.

Biological Networks for Cancer Candidate Biomarkers Discovery

Due to its extraordinary heterogeneity and complexity, cancer is often proposed as a model case of a systems biology disease or network disease. There is a critical need of effective biomarkers for cancer diagnosis and/or outcome prediction from system level analyses. Methods based on integrating omics data into networks have the potential to revolutionize the identification of cancer biomarkers. Deciphering the biological networks underlying cancer is undoubtedly important for understanding the molecular mechanisms of the disease and identifying effective biomarkers. In this review, the networks constructed for cancer biomarker discovery based on different omics level data are described and illustrated from recent advances in the field.

Integration and Visualization of Translational Medicine Data for Better Understanding of Human Diseases

Translational medicine is a domain turning results of basic life science research into new tools and methods in a clinical environment, for example, as new diagnostics or therapies. Nowadays, the process of translation is supported by large amounts of heterogeneous data ranging from medical data to a whole range of -omics data. It is not only a great opportunity but also a great challenge, as translational medicine big data is difficult to integrate and analyze, and requires the involvement of biomedical experts for the data processing. We show here that visualization and interoperable workflows, combining multiple complex steps, can address at least parts of the challenge. In this article, we present an integrated workflow for exploring, analysis, and interpretation of translational medicine data in the context of human health. Three Web services-tranSMART, a Galaxy Server, and a MINERVA platform-are combined into one big data pipeline. Native visualization capabilities enable the biomedical experts to get a comprehensive overview and control over separate steps of the workflow. The capabilities of tranSMART enable a flexible filtering of multidimensional integrated data sets to create subsets suitable for downstream processing. A Galaxy Server offers visually aided construction of analytical pipelines, with the use of existing or custom components. A MINERVA platform supports the exploration of health and disease-related mechanisms in a contextualized analytical visualization system. We demonstrate the utility of our workflow by illustrating its subsequent steps using an existing data set, for which we propose a filtering scheme, an analytical pipeline, and a corresponding visualization of analytical results. The workflow is available as a sandbox environment, where readers can work with the described setup themselves. Overall, our work shows how visualization and interfacing of big data processing services facilitate exploration, analysis, and interpretation of translational medicine data.

Systematic pan-cancer analysis of tumour purity

The tumour microenvironment is the non-cancerous cells present in and around a tumour, including mainly immune cells, but also fibroblasts and cells that comprise supporting blood vessels. These non-cancerous components of the tumour may play an important role in cancer biology. They also have a strong influence on the genomic analysis of tumour samples, and may alter the biological interpretation of results. Here we present a systematic analysis using different measurement modalities of tumour purity in >10,000 samples across 21 cancer types from the Cancer Genome Atlas. Patients are stratified according to clinical features in an attempt to detect clinical differences driven by purity levels. We demonstrate the confounding effect of tumour purity on correlating and clustering tumours with transcriptomics data. Finally, using a differential expression method that accounts for tumour purity, we find an immunotherapy gene signature in several cancer types that is not detected by traditional differential expression analyses.

The importance of the tumour microenvironment has now been realised, however the presence of non-tumour cells in cancer samples can complicate genomic analyses. Here, the authors estimate tumour purity in 10,000 samples from the TCGA dataset and can detect a signature of T cell activation.

Evidence of perturbations of the cytokine network in preterm labor

OBJECTIVE

Intraamniotic inflammation/infection is the only mechanism of disease with persuasive evidence of causality for spontaneous preterm labor/delivery. Previous studies about the behavior of cytokines in preterm labor have been largely based on the analysis of the behavior of each protein independently. Emerging evidence indicates that the study of biologic networks can provide insight into the pathobiology of disease and improve biomarker discovery. The goal of this study was to characterize the inflammatory-related protein network in the amniotic fluid of patients with preterm labor.

STUDY DESIGN

A retrospective cohort study was conducted that included women with singleton pregnancies who had spontaneous preterm labor and intact membranes (n = 135). These patients were classified according to the results of amniotic fluid culture, broad-range polymerase chain reaction coupled with electrospray ionization mass spectrometry, and amniotic fluid concentration of interleukin (IL)-6 into the following groups: (1) those without intraamniotic inflammation (n = 85), (2) those with microbial-associated intraamniotic inflammation (n = 15), and (3) those with intraamniotic inflammation without detectable bacteria (n = 35). Amniotic fluid concentrations of 33 inflammatory-related proteins were determined with the use of a multiplex bead array assay.

RESULTS

Patients with preterm labor and intact membranes who had microbial-associated intraamniotic inflammation had a higher amniotic fluid inflammatory-related protein concentration correlation than those without intraamniotic inflammation (113 perturbed correlations). IL-1β, IL-6, macrophage inflammatory protein (MIP)-1α, and IL-1α were the most connected nodes (highest degree) in this differential correlation network (degrees of 20, 16, 12, and 12, respectively). Patients with sterile intraamniotic inflammation had correlation patterns of inflammatory-related proteins, both increased and decreased, when compared to those without intraamniotic inflammation (50 perturbed correlations). IL-1α, MIP-1α, and IL-1β were the most connected nodes in this differential correlation network (degrees of 12, 10, and 7, respectively). There were more coordinated inflammatory-related protein concentrations in the amniotic fluid of women with microbial-associated intraamniotic inflammation than in those with sterile intraamniotic inflammation (60 perturbed correlations), with IL-4 and IL-33 having the largest number of perturbed correlations (degrees of 15 and 13, respectively).

CONCLUSIONS

We report for the first time an analysis of the inflammatory-related protein network in spontaneous preterm labor. Patients with preterm labor and microbial-associated intraamniotic inflammation had more coordinated amniotic fluid inflammatory-related proteins than either those with sterile intraamniotic inflammation or those without intraamniotic inflammation. The correlations were also stronger in patients with sterile intraamniotic inflammation than in those without intraamniotic inflammation. The findings herein could be of value in the development of biomarkers of preterm labor.

Big Data in Pharmaceutical R&D: Creating a Sustainable R&D Engine

Over the last 20 years, productivity in the pharmaceutical industry has been diminishing because of constantly increasing costs while output has overall been stagnant. Despite many efforts, productivity remains a challenge within the industry. At the same time, healthcare providers quite rightly require better value for money and clear evidence that new drugs are better than the current standard of care, making a complex situation even more complex. With the implementation of ‘Big Data’ initiatives trying to integrate data from disparate data sources and disciplines that are available in life science, the industry has identified a new frontier that might provide the insights needed to turn the ship around and allow the industry to return to sustainable growth.