Biomarker correlation network in colorectal carcinoma by tumor anatomic location

Current and future directions in network biology

Summary

Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology, focusing on molecular/cellular networks but also on other biological network types such as biomedical knowledge graphs, patient similarity networks, brain networks, and social/contact networks relevant to disease spread. In more detail, we highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on future directions of network biology. Additionally, we discuss scientific communities, educational initiatives, and the importance of fostering diversity within the field. This article establishes a roadmap for an immediate and long-term vision for network biology.

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Large airway T cells in adults with former bronchopulmonary dysplasia

BACKGROUND

Bronchopulmonary Dysplasia (BPD) in infants born prematurely is a risk factor for chronic airway obstruction later in life. The distribution of T cell subtypes in the large airways is largely unknown.

OBJECTIVE

To characterize cellular and T cell profiles in the large airways of young adults with a history of BPD.

METHODS

Forty-three young adults born prematurely (preterm (n = 20), BPD (n = 23)) and 45 full-term-born (asthma (n = 23), healthy (n = 22)) underwent lung function measurements, and bronchoscopy with large airway bronchial wash (BW). T-cells subsets in BW were analyzed by immunocytochemistry.

RESULTS

The proportions of both lymphocytes and CD8 + T cells in BW were significantly higher in BPD (median, 6.6%, and 78.0%) when compared with asthma (3.4% and 67.8%, p = 0.002 and p = 0.040) and healthy (3.8% and 40%, p < 0.001 and p < 0.001). In all adults born prematurely (preterm and BPD), lymphocyte proportion correlated negatively with forced vital capacity (r= -0.324, p = 0.036) and CD8 + T cells correlated with forced expiratory volume in one second, FEV1 (r=-0.448, p = 0.048). Correlation-based network analysis revealed that lung function cluster and BPD-birth cluster were associated with lymphocytes and/or CD4 + and CD8 + T cells. Multivariate regression analysis showed that lymphocyte proportions and BPD severity qualified as independent factors associated with FEV1.

CONCLUSIONS

The increased cytotoxic T cells in the large airways in young adults with former BPD, suggest a similar T-cell subset pattern as in the small airways, resembling features of COPD. Our findings strengthen the hypothesis that mechanisms involving adaptive and innate immune responses are involved in the development of airway disease due to preterm birth.

Metabolic Connectome and Its Role in the Prediction, Diagnosis, and Treatment of Complex Diseases

The interconnectivity of advanced biological systems is essential for their proper functioning. In modern connectomics, biological entities such as proteins, genes, RNA, DNA, and metabolites are often represented as nodes, while the physical, biochemical, or functional interactions between them are represented as edges. Among these entities, metabolites are particularly significant as they exhibit a closer relationship to an organism's phenotype compared to genes or proteins. Moreover, the metabolome has the ability to amplify small proteomic and transcriptomic changes, even those from minor genomic changes. Metabolic networks, which consist of complex systems comprising hundreds of metabolites and their interactions, play a critical role in biological research by mediating energy conversion and chemical reactions within cells. This review provides an introduction to common metabolic network models and their construction methods. It also explores the diverse applications of metabolic networks in elucidating disease mechanisms, predicting and diagnosing diseases, and facilitating drug development. Additionally, it discusses potential future directions for research in metabolic networks. Ultimately, this review serves as a valuable reference for researchers interested in metabolic network modeling, analysis, and their applications.

Emerging role and clinical implication of mRNA scavenger decapping enzyme in colorectal cancer

BACKGROUND

Turnover of RNA is a regulated process that in part controls gene expression. This process is partly controlled by the scavenger decapping enzyme (DcpS). This study aimed to investigate the expression of DcpS in colorectal cancer (CRC) tissue, to evaluate its prognostic significance in patients with CRC and to investigate potentially targeted genes by DcpS.

METHODS

Immunohistochemical analysis was used to determine localization of DcpS in normal and CRC tissue, western blot analysis for quantification of protein expression and qPCR for mRNA expression in normal and CRC tissue and expression in cell lines after silencing using siRNA. Gene array analysis was used to study regulation of genes after silencing of DcpS. Proliferation was studied using BRDU.

RESULTS

DcpS expression was localized to the epithelial cells of both control and cancer tissue. Tumor and paired control tissue samples from 100 patients who underwent surgical resection for primary colorectal adenocarcinomas were utilized. mRNA and protein of DcpS was significantly up-regulated in the patients with CRC and the mRNA level was higher in rectal cancer tissue compared to colon cancer tissue (p < 0.05). Lowest tertile levels of DcpS mRNA in cancer tissue was associated with a decreased cancer-specific survival rate with a hazard ratio (HR) of 4.7 (95% CI=1.02-12.3), independent of disease stage. The low level of DcpS mRNA was a predictor of poorer survival in patients with rectal and disseminated cancer and in patients receiving adjuvant treatment (p < 0.05). After silencing DcpS in Caco-2 cancer cells, altered expression of several genes associated with RNA, cell cycle regulation, alternative splicing and microRNA was observed and resulted in 23% increase in proliferation.

CONCLUSIONS

These results indicate that DcpS has potential as a prognostic factor for CRC but further studies in a broader cohort are warranted to evaluate the significance of the findings in the clinic.

Networks as Biomarkers: Uses and Purposes

Networks-based approaches are often used to analyze gene expression data or protein-protein interactions but are not usually applied to study the relationships between different biomarkers. Given the clinical need for more comprehensive and integrative biomarkers that can help to identify personalized therapies, the integration of biomarkers of different natures is an emerging trend in the literature. Network analysis can be used to analyze the relationships between different features of a disease; nodes can be disease-related phenotypes, gene expression, mutational events, protein quantification, imaging-derived features and more. Since different biomarkers can exert causal effects between them, describing such interrelationships can be used to better understand the underlying mechanisms of complex diseases. Networks as biomarkers are not yet commonly used, despite being proven to lead to interesting results. Here, we discuss in which ways they have been used to provide novel insights into disease susceptibility, disease development and severity.

Effects of novel SGLT2 inhibitors on cancer incidence in hyperglycemic patients: a meta-analysis of randomized clinical trials

Epidemiological evidence shows that diabetic patients have an increased cancer risk and a higher mortality rate. Glucose could play a central role in metabolism and growth of many tumor types, and this possible mechanism is supported by the high rate of glucose demand and uptake in cancer. Thus, growing evidence suggests that hyperglycemia contributes to cancer progression but also to its onset. Many mechanisms underlying this association have been hypothesized, such as insulin resistance, hyperinsulinemia, and increased inflammatory processes. Inflammation is a common pathophysiological feature in both diabetic and oncological patients, and inflammation linked to high glucose levels sensitizes microenvironment to tumorigenesis, promoting the development of malignant lesions by altering and sustaining a pathological condition in tissues. Glycemic control is the first goal of antidiabetic therapy, and glucose level reduction has also been associated with favorable outcomes in cancer. Here, we describe key events in carcinogenesis focusing on hyperglycemia as supporter in tumor progression and in particular, related to the role of a specific hypoglycemic drug class, sodium-glucose linked transporters (SGLTs). We also discuss the use of SGLT2 inhibitors as a novel potential cancer therapy. Our meta-analysis showed that SGLT-2 inhibitors were significantly associated with an overall reduced risk of cancer as compared to placebo (RR = 0.35, CI 0.33-0.37, P = 0. 00) with a particular effectiveness for dapaglifozin and ertuglifozin (RR = 0. 06, CI 0. 06-0. 07 and RR = 0. 22, CI 0. 18-0. 26, respectively). Network Medicine approaches may advance the possible repurposing of these drugs in patients with concomitant diabetes and cancer.

Association of gene and protein expression and genetic polymorphism of CC chemokine ligand 4 in colorectal cancer

BACKGROUND

Leukocytes, such as T cells and macrophages, play an important role in tumorigenesis. CC chemokine ligand (CCL) 4, which is produced by lymphocytes and macrophages, has been found to be expressed in the mucosa of the gastrointestinal tract and is a potent chemoattractant for various leukocytes.

AIM

To examine CCL4 expression and its genetic polymorphism rs10491121 in patients with colorectal cancer (CRC) and evaluate their prognostic significance.

METHODS

Luminex technology was used to determine CCL4 Levels in CRC tissue (n = 98), compared with paired normal tissue, and in plasma from patients with CRC (n = 103), compared with healthy controls (n = 97). Included patients had undergone surgical resection for primary colorectal adenocarcinomas between 1996 and 2019 at the Department of Surgery, Ryhov County Hospital, Jönköping, Sweden. Reverse transcription quantitative PCR was used to investigate the CCL4 gene expression in CRC tissue (n = 101). Paired normal tissue and TaqMan single nucleotide polymorphism assays were used for the CCL4 rs10491121 polymorphism in 610 CRC patients and 409 healthy controls.

RESULTS

The CCL4 protein and messenger RNA expression levels were higher in CRC tissue than in normal paired tissue (90%, P < 0.001 and 45%, P < 0.05, respectively). CRC tissue from patients with localized disease had 2.8-fold higher protein expression levels than that from patients with disseminated disease. Low CCL4 protein expression levels in CRC tissue were associated with a 30% lower cancer-specific survival rate in patients (P < 0.01). The level of plasma CCL4 was 11% higher in CRC patients than in healthy controls (P < 0.05) and was positively correlated (r = 0.56, P < 0.01) with the CCL4 protein level in CRC tissue. The analysis of CCL4 gene polymorphism rs10491121 showed a difference (P < 0.05) between localized disease and disseminated disease in the right colon, with a dominance of allele A in localized disease. Moreover, the rate of the A allele was higher among CRC patients with mucinous cancer than among those with non-mucinous cancer.

CONCLUSION

The present study indicates that the CRC tissue levels of CCL4 and CCL4 gene polymorphism rs10491121, particularly in the right colon, are associated with clinical outcome in CRC patients.

Survival Benefit of Palliative Primary Tumor Resection Based on Tumor Location in Patients with Metastatic Colorectal Cancer: A Single-center Retrospective Study

Backgrounds/Aims

The molecular underpinnings of colorectal cancer (CRC) vary according to the tumor location. The advantages of a palliative primary tumor resection in patients with metastatic CRC are controversial. This study examined the survival outcomes of a palliative primary tumor resection based on the tumor location in patients with metastatic CRC.

Methods

The medical records of 600 patients diagnosed with metastatic CRC between January 2000 and June 2018 were reviewed retrospectively. Patients undergoing surgery for both the primary tumor and metastatic lesions were excluded. The clinical factors affecting the long-term outcomes were evaluated according to the primary tumor location, and the long-term survival was compared between patients with and without a palliative primary tumor resection. The data were analyzed using the Kaplan-Meier estimator and multivariate Cox regression models.

Results

The median follow-up duration was 18 months (interquartile range, 10-28). Patients with right-sided CRC had a poor overall- and progression-free survival compared to those with left-sided CRC. In multivariate Cox regression analysis, the palliative primary tumor resection was an independent prognostic factor predicting better overall survival in patients with metastatic CRC, regardless of the primary tumor location.

Conclusions

The primary tumor location influences the prognosis, and that a primary tumor resection can improve the overall survival in patients with metastatic CRC, regardless of the primary tumor location.

Cytotoxic T-lymphocyte Antigen-4 (CTLA-4) Gene Polymorphism (rs3087243) Is Related to Risk and Survival in Patients With Colorectal Cancer

BACKGROUND/AIM

Cytotoxic T-lymphocyte antigen-4 (CTLA-4), transiently expressed on T cells, plays a pivotal role in the negative feedback regulation of T-cell activation and proliferation. The aim of the present study was to examine the influence of CTLA-4 gene polymorphism rs3087243 on CRC susceptibility and long-term survival in Swedish patients with CRC.

PATIENTS AND METHODS

Genotypes of 491 patients and 433 healthy controls were determined, using TaqMan single nucleotide polymorphism (SNP) assays based on polymerase chain reaction.

RESULTS

Patients carrying allele A were found to be at a higher risk of CRC and this allele was found to be more common in patients with disseminated disease compared to localized disease in the right colon. Kaplan-Meier analysis of cancer-specific survival showed that carriers of allele A had the highest risk of CRC-related death.

CONCLUSION

The SNP rs3087243 of the CTLA-4 gene was associated with CRC risk and, therefore, it could be a prognostic marker for Swedish patients with CRC.

Progressive alteration of DNA methylation of Alu, MGMT, MINT2, and TFPI2 genes in colonic mucosa during colorectal cancer development

BACKGROUND

Colorectal cancer (CRC) is one of the leading causes of mortality and morbidity in the world. It is characterized by different pathways of carcinogenesis and is a heterogeneous disease with diverse molecular landscapes that reflect histopathological and clinical information. Changes in the DNA methylation status of colon epithelial cells have been identified as critical components in CRC development and appear to be emerging biomarkers for the early detection and prognosis of CRC.

OBJECTIVE

To explore the underlying disease mechanisms and identify more effective biomarkers of CRC.

METHODS

We compared the levels and frequencies of DNA methylation in 11 genes (Alu, APC, DAPK, MGMT, MLH1, MINT1, MINT2, MINT3, p16, RGS6, and TFPI2) in colorectal cancer and its precursor adenomatous polyp with normal tissue of healthy subjects using pyrosequencing and then evaluated the clinical value of these genes.

RESULTS

Aberrant methylation of Alu, MGMT, MINT2, and TFPI2 genes was progressively accumulated during the normal-adenoma-carcinoma progression. Additionally, CGI methylation occurred either as an adenoma-associated event for APC, MLH1, MINT1, MINT31, p16, and RGS6 or a tumor-associated event for DAPK. Moreover, relatively high levels and frequencies of DAPK, MGMT, and TFPI2 methylation were detected in the peritumoral nonmalignant mucosa of cancer patients in a field-cancerization manner, as compared to normal mucosa from healthy subjects.

CONCLUSION

This study identified several biomarkers associated with the initiation and progression of CRC. As novel findings, they may have important clinical implications for CRC diagnostic and prognostic applications. Further large-scale studies are needed to confirm these findings.

Molecular networks in Network Medicine: Development and applications

Network Medicine applies network science approaches to investigate disease pathogenesis. Many different analytical methods have been used to infer relevant molecular networks, including protein-protein interaction networks, correlation-based networks, gene regulatory networks, and Bayesian networks. Network Medicine applies these integrated approaches to Omics Big Data (including genetics, epigenetics, transcriptomics, metabolomics, and proteomics) using computational biology tools and, thereby, has the potential to provide improvements in the diagnosis, prognosis, and treatment of complex diseases. We discuss briefly the types of molecular data that are used in molecular network analyses, survey the analytical methods for inferring molecular networks, and review efforts to validate and visualize molecular networks. Successful applications of molecular network analysis have been reported in pulmonary arterial hypertension, coronary heart disease, diabetes mellitus, chronic lung diseases, and drug development. Important knowledge gaps in Network Medicine include incompleteness of the molecular interactome, challenges in identifying key genes within genetic association regions, and limited applications to human diseases. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Translational, Genomic, and Systems Medicine > Translational Medicine Analytical and Computational Methods > Analytical Methods Analytical and Computational Methods > Computational Methods.

A correlation-based network for biomarker discovery in obesity with metabolic syndrome

Background

Obesity is associated with chronic activation of the immune system and an altered gut microbiome, leading to increased risk of chronic disease development. As yet, no biomarker profile has been found to distinguish individuals at greater risk of obesity-related disease. The aim of this study was to explore a correlation-based network approach to identify existing patterns of immune-microbiome interactions in obesity.

Results

The current study performed correlation-based network analysis on five different datasets obtained from 11 obese with metabolic syndrome (MetS) and 12 healthy weight men. These datasets included: anthropometric measures, metabolic measures, immune cell abundance, serum cytokine concentration, and gut microbial composition. The obese with MetS group had a denser network (total number of edges, n = 369) compared to the healthy network (n = 299). Within the obese with MetS network, biomarkers from the immune cell abundance group was found to be correlated to biomarkers from all four other datasets. Conversely in the healthy network, immune cell abundance was only correlated with serum cytokine concentration and gut microbial composition. These observations suggest high involvement of immune cells in obese with MetS individuals. There were also three key hubs found among immune cells in the obese with MetS networks involving regulatory T cells, neutrophil and cytotoxic cell abundance. No hubs were present in the healthy network.

Conclusion

These results suggest a more complex interaction of inflammatory markers in obesity, with high connectivity of immune cells in the obese with MetS network compared to the healthy network. Three key hubs were identified in the obese with MetS network, involving Treg, neutrophils and cytotoxic cell abundance. Compared to a t-test, the network approach offered more meaningful results when comparing obese with MetS and healthy weight individuals, demonstrating its superiority in exploratory analysis.

A Network Analysis of Biomarkers for Type 2 Diabetes

Numerous studies have investigated individual biomarkers in relation to risk of type 2 diabetes. However, few have considered the interconnectivity of these biomarkers in the etiology of diabetes as well as the potential changes in the biomarker correlation network during diabetes development. We conducted a secondary analysis of 27 plasma biomarkers representing glucose metabolism, inflammation, adipokines, endothelial dysfunction, IGF axis, and iron store plus age and BMI at blood collection from an existing case-control study nested in the Nurses' Health Study (NHS), including 1,303 incident diabetes case subjects and 1,627 healthy women. A correlation network was constructed based on pairwise Spearman correlations of the above factors that were statistically different between case and noncase subjects using permutation tests (P < 0.0005). We further evaluated the network structure separately among diabetes case subjects diagnosed <5, 5-10, and >10 years after blood collection versus noncase subjects. Although pairwise biomarker correlations tended to have similar directions comparing diabetes case subjects to noncase subjects, most correlations were stronger in noncase than in case subjects, with the largest differences observed for the insulin/HbA_1c and leptin/adiponectin correlations. Leptin and soluble leptin receptor were two hubs of the network, with large numbers of different correlations with other biomarkers in case versus noncase subjects. When examining the correlation network by timing of diabetes onset, there were more perturbations in the network for case subjects diagnosed >10 years versus <5 years after blood collection, with consistent differential correlations of insulin and HbA_1c C-peptide was the most highly connected node in the early-stage network, whereas leptin was the hub for mid- or late-stage networks. Our results suggest that perturbations of the diabetes-related biomarker network may occur decades prior to clinical recognition. In addition to the persistent dysregulation between insulin and HbA_1c, our results highlight the central role of the leptin system in diabetes development.

Insights into Pathogenic Interactions Among Environment, Host, and Tumor at the Crossroads of Molecular Pathology and Epidemiology

Evidence indicates that diet, nutrition, lifestyle, the environment, the microbiome, and other exogenous factors have pathogenic roles and also influence the genome, epigenome, transcriptome, proteome, and metabolome of tumor and nonneoplastic cells, including immune cells. With the need for big-data research, pathology must transform to integrate data science fields, including epidemiology, biostatistics, and bioinformatics. The research framework of molecular pathological epidemiology (MPE) demonstrates the strengths of such an interdisciplinary integration, having been used to study breast, lung, prostate, and colorectal cancers. The MPE research paradigm not only can provide novel insights into interactions among environment, tumor, and host but also opens new research frontiers. New developments-such as computational digital pathology, systems biology, artificial intelligence, and in vivo pathology technologies-will further transform pathology and MPE. Although it is necessary to address the rarity of transdisciplinary education and training programs, MPE provides an exemplary model of integrative scientific approaches and contributes to advancements in precision medicine, therapy, and prevention.

HC-HDSD: A method of hypergraph construction and high-density subgraph detection for inferring high-order epistatic interactions

Detecting epistatic interactions, or nonlinear interactive effects of Single Nucleotide Polymorphisms (SNPs), has gained increasing attention in explaining the "missing heritability" of complex diseases. Though much work has been done in mapping SNPs underlying diseases, most of them constrain to 2-order epistatic interactions. In this paper, a method of hypergraph construction and high-density subgraph detection, named HC-HDSD, is proposed for detecting high-order epistatic interactions. The hypergraph is constructed by low-order epistatic interactions that identified using the normalized co-information measure and the exhaustive search. The hypergraph consists of two types of vertices: real ones representing main effects of SNPs and virtual ones denoting interactive effects of epistatic interactions. Then, both maximal clique centrality algorithm and near-clique mining algorithm are employed to detect high-density subgraphs from the constructed hypergraph. These high-density subgraphs are inferred as high-order epistatic interactions in the HC-HDSD. Experiments are performed on several simulation data sets, results of which show that HC-HDSD is promising in inferring high-order epistatic interactions while substantially reducing the computation cost. In addition, the application of HC-HDSD on a real Age-related Macular Degeneration (AMD) data set provides several new clues for the exploration of causative factors of AMD.

Integrative analysis of exogenous, endogenous, tumour and immune factors for precision medicine

Immunotherapy strategies targeting immune checkpoints such as the CTLA4 and CD274 (programmed cell death 1 ligand 1, PD-L1)/PDCD1 (programmed cell death 1, PD-1) T-cell coreceptor pathways are revolutionising oncology. The approval of pembrolizumab use for solid tumours with high-level microsatellite instability or mismatch repair deficiency by the US Food and Drug Administration highlights promise of precision immuno-oncology. However, despite evidence indicating influences of exogenous and endogenous factors such as diet, nutrients, alcohol, smoking, obesity, lifestyle, environmental exposures and microbiome on tumour-immune interactions, integrative analyses of those factors and immunity lag behind. Immune cell analyses in the tumour microenvironment have not adequately been integrated into large-scale studies. Addressing this gap, the transdisciplinary field of molecular pathological epidemiology (MPE) offers research frameworks to integrate tumour immunology into population health sciences, and link the exposures and germline genetics (eg, HLA genotypes) to tumour and immune characteristics. Multilevel research using bioinformatics, in vivo pathology and omics (genomics, epigenomics, transcriptomics, proteomics and metabolomics) technologies is possible with use of tissue, peripheral blood circulating cells, cell-free plasma, stool, sputum, urine and other body fluids. This immunology-MPE model can synergise with experimental immunology, microbiology and systems biology. GI neoplasms represent exemplary diseases for the immunology-MPE model, given rich microbiota and immune tissues of intestines, and the well-established carcinogenic role of intestinal inflammation. Proof-of-principle studies on colorectal cancer provided insights into immunomodulating effects of aspirin, vitamin D, inflammatory diets and omega-3 polyunsaturated fatty acids. The integrated immunology-MPE model can contribute to better understanding of environment-tumour-immune interactions, and effective immunoprevention and immunotherapy strategies for precision medicine.