Identification of Genetic and Epigenetic Variants Associated with Breast Cancer Prognosis by Integrative Bioinformatics Analysis

Moura A, Filho ODMM, Pessoa C, Miranda Furtado CL. Hypomethylation at H19DMR in penile squamous cell carcinoma is not related to HPV infection

Penile squamous cell carcinoma (SCC) is a rare and aggressive tumour mainly related to lifestyle behaviour and human papillomavirus (HPV) infection. Environmentally induced loss of imprinting (LOI) at the H19 differentially methylated region (H19DMR) is associated with many cancers in the early events of tumorigenesis and may be involved in the pathogenesis of penile SCC. We sought to evaluate the DNA methylation pattern at H19DMR and its association with HPV infection in men with penile SCC by bisulfite sequencing (bis-seq). We observed an average methylation of 32.2% ± 11.6% at the H19DMR of penile SCC and did not observe an association between the p16INK4a+ (p = 0.59) and high-risk HPV+ (p = 0.338) markers with methylation level. The average methylation did not change according to HPV positive for p16INK4a+ or hrHPV+ (35.4% ± 10%) and negative for both markers (32.4% ± 10.1%) groups. As the region analysed has a binding site for the CTCF protein, the hypomethylation at the surrounding CpG sites might alter its insulator function. In addition, there was a positive correlation between intense polymorphonuclear cell infiltration and hypomethylation at H19DMR (p = 0.035). Here, we report that hypomethylation at H19DMR in penile SCC might contribute to tumour progression and aggressiveness regardless of HPV infection.

Involvement of CCCTC-binding factor in epigenetic regulation of cancer

A major global health burden continues to be borne by the complex and multifaceted disease of cancer. Epigenetic changes, which are essential for the emergence and spread of cancer, have drawn a huge amount of attention recently. The CCCTC-binding factor (CTCF), which takes part in a wide range of cellular processes including genomic imprinting, X chromosome inactivation, 3D chromatin architecture, local modifications of histone, and RNA polymerase II-mediated gene transcription, stands out among the diverse array of epigenetic regulators. CTCF not only functions as an architectural protein but also modulates DNA methylation and histone modifications. Epigenetic regulation of cancer has already been the focus of plenty of studies. Understanding the role of CTCF in the cancer epigenetic landscape may lead to the development of novel targeted therapeutic strategies for cancer. CTCF has already earned its status as a tumor suppressor gene by acting like a homeostatic regulator of genome integrity and function. Moreover, CTCF has a direct effect on many important transcriptional regulators that control the cell cycle, apoptosis, senescence, and differentiation. As we learn more about CTCF-mediated epigenetic modifications and transcriptional regulations, the possibility of utilizing CTCF as a diagnostic marker and therapeutic target for cancer will also increase. Thus, the current review intends to promote personalized and precision-based therapeutics for cancer patients by shedding light on the complex interplay between CTCF and epigenetic processes.

Susceptibility to hypertension based on MTHFR rs1801133 single nucleotide polymorphism and MTHFR promoter methylation

Background

The aetio-pathologenesis of hypertension is multifactorial, encompassing genetic, epigenetic, and environmental factors. The combined effect of genetic and epigenetic changes on hypertension is not known. We evaluated the independent and interactive association of MTHFR rs1801133 single nucleotide polymorphism (SNP) and MTHFR promoter methylation with hypertension among Taiwanese adults.

Methods

We retrieved data including, MTHFR promoter methylation, MTHFR rs1801133 genotypes (CC, CT, and TT), basic demography, personal lifestyle habits, and disease history of 1,238 individuals from the Taiwan Biobank (TWB).

Results

The distributions of hypertension and MTHFR promoter methylation quartiles (β < 0.1338, 0.1338 ≤ β < 0.1385, 0.1385 ≤ β < 0.1423, and β ≥ 0.1423 corresponding to

Conclusion

Independently, rs1801133 TT was associated with a higher risk of hypertension, but methylation was not. Based on genotypes, lower methylation was dose-dependently associated with a higher risk of hypertension in individuals with the CC genotype. Our findings suggest that MTHFR rs1801133 and MTHFR promoter methylation could jointly influence hypertension susceptibility.

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Key Words

• MTHFR
• hypertension
• interaction
• promoter methylation
• rs1801133
• susceptibility

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Affiliation(s)

Ming-Huang Chiu Department of Pulmonology and Respiratory Care, Cathay General Hospital, Taipei City, Taiwan
Chia-Hsiu Chang Cardiovascular Center, Cathay General Hospital, Taipei City, Taiwan
Disline Manli Tantoh Department of Medical Imaging, Chung Shan Medical University Hospital, Taichung City, Taiwan Department of Public Health and Institute of Public Health, Chung Shan Medical University, Taichung City, Taiwan
Tsui-Wen Hsu Superintendent Office, Institute of Medicine, Cathay General Hospital, Taipei City, Taiwan
Chih-Hsuan Hsiao Department of Public Health and Institute of Public Health, Chung Shan Medical University, Taichung City, Taiwan
Ji-Han Zhong Department of Public Health and Institute of Public Health, Chung Shan Medical University, Taichung City, Taiwan
Yung-Po Liaw Department of Medical Imaging, Chung Shan Medical University Hospital, Taichung City, Taiwan Department of Public Health and Institute of Public Health, Chung Shan Medical University, Taichung City, Taiwan Institute of Medicine, Chung Shan Medical University, Taichung City, Taiwan

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Interaction between a diabetes-related methylation site (TXNIP cg19693031) and variant (GLUT1 rs841853) on fasting blood glucose levels among non-diabetics

Background

Type 2 diabetes mellitus (T2DM) is caused by a combination of environmental, genetic, and epigenetic factors including, fasting blood glucose (FBG), genetic variant rs841853, and cg19693031 methylation. We evaluated the interaction between rs841853 and cg19693031 on the FBG levels of non-diabetic Taiwanese adults.

Methods

We used Taiwan Biobank (TWB) data collected between 2008 and 2016. The TWB data source contains information on basic demographics, personal lifestyles, medical history, methylation, and genotype. The study participants included 1300 people with DNA methylation data. The association of cg19693031 methylation (stratified into quartiles) with rs841853 and FBG was determined using multiple linear regression analysis. The beta-coefficients (β) and p-values were estimated.

Results

The mean ± standard deviation (SD) of FBG in rs841853-CC individuals (92.07 ± 7.78) did not differ significantly from that in the CA + AA individuals (91.62 ± 7.14). However, the cg19693031 methylation levels were significantly different in the two groups (0.7716 ± 0.05 in CC individuals and 0.7631 ± 0.05 in CA + AA individuals (p = 0.002). The cg19693031 methylation levels according to quartiles were β < 0.738592 (< Q1), 0.738592 ≤ 0.769992 (Q1–Q2), 0.769992 ≤ 0.800918 (Q2–Q3), and β ≥ 0.800918 (≥ Q3). FBG increased with decreasing cg19693031 methylation levels in a dose–response manner (p_trend = 0.005). The β-coefficient was − 0.0236 (p = 0.965) for Q2–Q3, 1.0317 (p = 0.058) for Q1–Q2, and 1.3336 (p = 0.019 for < Q1 compared to the reference quartile (≥ Q3). The genetic variant rs841853 was not significantly associated with FBG. However, its interaction with cg19693031 methylation was significant (p-value = 0.036). Based on stratification by rs841853 genotypes, only the CC group retained the inverse and dose–response association between FBG and cg19693031 methylation. The β (p-value) was 0.8082 (0.255) for Q2–Q3, 1.6930 (0.022) for Q1–Q2, and 2.2190 (0.004) for < Q1 compared to the reference quartile (≥ Q3). The p_trend was 0.002.

Conclusion

Summarily, methylation at cg19693031 was inversely associated with fasting blood glucose in a dose-dependent manner. The inverse association was more prominent in rs841853-CC individuals, suggesting that rs841853 could modulate the association between cg19693031 methylation and FBG. Our results suggest that genetic variants may be involved in epigenetic mechanisms associated with FBG, a hallmark of diabetes. Therefore, integrating genetic and epigenetic data may provide more insight into the early-onset of diabetes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03269-y.

Can the epigenome contribute to risk stratification for cancer onset?

The increasing burden of cancer requires identifying and protecting individuals at highest risk. The epigenome provides an indispensable complement to genetic alterations for a risk stratification approach for the following reasons: gene transcription necessary for cancer onset is directed by epigenetic modifications and many risk factors studied so far have been associated with alterations related to the epigenome. The risk level depends on the plasticity of the epigenome during phases of life particularly sensitive to environmental and dietary impacts. Modifications in the activity of DNA regulatory regions and altered chromatin compaction may accumulate, hence leading to the increase of cancer risk. Moreover, tissue architecture directs the unique organization of the epigenome for each tissue and cell type, which allows the epigenome to control cancer risk in specific organs. Investigations of epigenetic signatures of risk should help identify a continuum of alterations leading to a threshold beyond which the epigenome cannot maintain homeostasis. We propose that this threshold may be similar in the population for a given tissue, but the pace to reach this threshold will depend on the combination of germline inheritance and the risk and protective factors encountered, particularly during windows of epigenetic susceptibility, by individuals.

Determination of Genetic and Epigenetic Modifications-Related Prognostic Biomarkers of Breast Cancer: Genome High-Throughput Data Analysis

The high heterogeneity of breast cancer (BRCA) makes it more challenging to interpret the genetic variation mechanisms involved in BRCA pathogenesis and prognosis. Areas with high DNA methylation (such as CpG islands) were accompanied by copy number variation (CNV), and these genomic variations affected the level of DNA methylation. In this study, we characterized intertumor heterogeneity and analyzed the effects of CNV on DNA methylation and gene expression. In addition, we performed a Genetic Set Enrichment Analysis (GSEA) to identify key pathways for changes between patients with low and high expression of genes. Our analysis found two key genes, namely, HPDL and SOX17. The protein expressed by HPDL is 4-hydroxyphenylpyruvate dioxygenase-like protein, which has dioxygenase activity. SOX17 is a transcription factor that can inhibit Wnt signaling, promote the degradation of activated CTNNB1, and participate in cell proliferation. Our analysis found that the CNV of HPDL and SOX17 is not only related to the patient's prognosis, but also related to gene methylation and expression levels affecting the patient's survival time. Among them, the high-methylation, low-expression HPDL and SOX17 showed poor prognosis. And the addition of two copies of SOX17 is associated with a lower survival rate, while a decrease in the copy number of HPDL also suggests a poor prognosis. This study provided an effective bioinformatics basis for further exploration of molecular mechanisms related to BRCA and assessment of patient prognosis, but the development of biomarkers for diagnosis and treatment still requires further clinical data validation.

Protein profile of MCF-7 breast cancer cell line treated with lectin delivered by CaCO3NPs revealed changes in molecular chaperones, cytoskeleton, and membrane-associated proteins

The second most predominant cancer in the world and the first among women is breast cancer. We aimed to study the protein abundance profiles induced by lectin purified from the Agaricus bisporus mushroom (ABL) and conjugated with CaCO₃NPs in the MCF-7 breast cancer cell line. Two-dimensional electrophoresis (2-DE) and orbitrap mass spectrometry techniques were used to reveal the protein abundance pattern induced by lectin. Flow cytometric analysis showed the accumulation of ABL-CaCO₃NPs treated cells in the G1 phase than the positive control. Thirteen proteins were found different in their abundance in breast cancer cells after 24 h exposure to lectin conjugated with CaCO₃NPs. Most of the identified proteins were showing a low abundance in ABL-CaCO₃NPs treated cells in comparison to the positive and negative controls, including V-set and immunoglobulin domain, serum albumin, actin cytoplasmic 1, triosephosphate isomerase, tropomyosin alpha-4 chain, and endoplasmic reticulum chaperone BiP. Hornerin, tropomyosin alpha-1 chain, annexin A2, and protein disulfide-isomerase were up-regulated in comparison to the positive. Bioinformatic analyses revealed the regulation changes of these proteins mainly affected the pathways of 'Bcl-2-associated athanogene 2 signalling pathway', 'Unfolded protein response', 'Caveolar-mediated endocytosis signalling', 'Clathrin-mediated endocytosis signalling', 'Calcium signalling' and 'Sucrose degradation V', which are associated with breast cancer. We concluded that lectin altered the abundance in molecular chaperones/heat shock proteins, cytoskeletal, and metabolic proteins. Additionally, lectin induced a low abundance of MCF-7 cancer cell proteins in comparison to the positive and negative controls, including; V-set and immunoglobulin domain, serum albumin, actin cytoplasmic 1, triosephosphate isomerase, tropomyosin alpha-4 chain, and endoplasmic reticulum chaperone BiP.

The Clinical and Pathological Profile of BRCA1 Gene Methylated Breast Cancer Women: A Meta-Analysis

BACKGROUND

DNA aberrant hypermethylation is the major cause of transcriptional silencing of the breast cancer gene 1 (BRCA1) gene in sporadic breast cancer patients. The aim of the present meta-analysis was to analyze all available studies reporting clinical characteristics of BRCA1 gene hypermethylated breast cancer in women, and to pool the results to provide a unique clinical profile of this cancer population.

METHODS

On September 2020, a systematic literature search was performed. Data were retrieved from PubMed, MEDLINE, and Scopus by searching the terms: "BRCA*" AND "methyl*" AND "breast". All studies evaluating the association between BRCA1 methylation status and breast cancer patients' clinicopathological features were considered for inclusion.

RESULTS

465 studies were retrieved. Thirty (6.4%) studies including 3985 patients met all selection criteria. The pooled analysis data revealed a significant correlation between BRCA1 gene hypermethylation and advanced breast cancer disease stage (OR = 0.75: 95% CI: 0.58-0.97; p = 0.03, fixed effects model), lymph nodes involvement (OR = 1.22: 95% CI: 1.01-1.48; p = 0.04, fixed effects model), and pre-menopausal status (OR = 1.34: 95% CI: 1.08-1.66; p = 0.008, fixed effects model). No association could be found between BRCA1 hypermethylation and tumor histology (OR = 0.78: 95% CI: 0.59-1.03; p = 0.08, fixed effects model), tumor grading (OR = 0.78: 95% CI :0.46-1.32; p = 0.36, fixed effects model), and breast cancer molecular classification (OR = 1.59: 95% CI: 0.68-3.72; p = 0.29, random effects model).

CONCLUSIONS

hypermethylation of the BRCA1 gene significantly correlates with advanced breast cancer disease, lymph nodes involvement, and pre-menopausal cancer onset.

Deciphering the genetic code of DNA methylation

DNA methylation plays crucial roles in many biological processes and abnormal DNA methylation patterns are often observed in diseases. Recent studies have shed light on cis-acting DNA elements that regulate locus-specific DNA methylation, which involves transcription factors, histone modification and DNA secondary structures. In addition, several recent studies have surveyed DNA motifs that regulate DNA methylation and suggest potential applications in diagnosis and prognosis. Here, we discuss the current biological foundation for the cis-acting genetic code that regulates DNA methylation. We review the computational models that predict DNA methylation with genetic features and discuss the biological insights revealed from these models. We also provide an in-depth discussion on how to leverage such knowledge in clinical applications, particularly in the context of liquid biopsy for early cancer diagnosis and treatment.

Gene-methylation interactions: discovering region-wise DNA methylation levels that modify SNP-associated disease risk

BACKGROUND

Current technology allows rapid assessment of DNA sequences and methylation levels at a single-site resolution for hundreds of thousands of sites in the human genome, in thousands of individuals simultaneously. This has led to an increase in epigenome-wide association studies (EWAS) of complex traits, particularly those that are poorly explained by previous genome-wide association studies (GWAS). However, the genome and epigenome are intertwined, e.g., DNA methylation is known to affect gene expression through, for example, genomic imprinting. There is thus a need to go beyond single-omics data analyses and develop interaction models that allow a meaningful combination of information from EWAS and GWAS.

RESULTS

We present two new methods for genetic association analyses that treat offspring DNA methylation levels as environmental exposure. Our approach searches for statistical interactions between SNP alleles and DNA methylation (G ×Me) and between parent-of-origin effects and DNA methylation (PoO ×Me), using case-parent triads or dyads. We use summarized methylation levels over nearby genomic region to ease biological interpretation. The methods were tested on a dataset of parent-offspring dyads, with EWAS data on the offspring. Our results showed that methylation levels around a SNP can significantly alter the estimated relative risk. Moreover, we show how a control dataset can identify false positives.

CONCLUSIONS

The new methods, G ×Me and PoO ×Me, integrate DNA methylation in the assessment of genetic relative risks and thus enable a more comprehensive biological interpretation of genome-wide scans. Moreover, our strategy of condensing DNA methylation levels within regions helps overcome specific disadvantages of using sparse chip-based measurements. The methods are implemented in the freely available R package Haplin ( https://cran.r-project.org/package=Haplin ), enabling fast scans of multi-omics datasets.

A Comprehensive Risk Assessment Model for Ovarian Cancer Patients with Phospho-STAT3 and IL-31 as Immune Infiltration Relevant Genes

Introduction

Ovarian carcinoma is a malignant tumor with a high mortality rate and a lack of effective treatment options for patients at advanced stages. For improving outcomes and helping patients with poor prognosis, choose a suitable therapy and an excellent risk assessment model and new treatment options are needed.

Materials and Methods

Ovarian cancer gene expression profile of GSE32062 was downloaded from the NCBI GEO database for screening differentially expressed genes (DEGs) between well and poor prognosis groups using limma package in R (version 3.4.1). Prognosis-related genes and clinical prognostic factors were obtained from univariate and multivariate Cox regression analyses, and a comprehensive risk assessment model was constructed using a Pathway Dysregulation Score (PDS) matrix, Cox-Proportional Hazards (Cox-PH) regression, as well as L1-least absolute shrinkage and selection operator (L1-LASSO) penalization. Then, significant DEGs were converted to pathways and optimal prognosis-related pathways were screened. Finally, risk prediction models based on pathways, genes involved in pathways, and comprehensive clinical risk factors with pathways were built. Their prognostic functions were assessed in verification sets. Besides, genes involved in immune-pathways were checked for immune infiltration using immunohistochemistry.

Results

A superior risk assessment model involving 9 optimal combinations of pathways and one clinical factor was constructed. The pathway-based model was found to be superior to the gene-based model. Phospho-STAT3 (from JAK-STAT signaling pathway) and IL-31 (from DEGs) were found to be related to immune infiltration.

Conclusion

We have generated a comprehensive risk assessment model consisting of a clinical risk factor and pathways that showed a possible bright foreground. The set of significant pathways might play as a better prognosis model which is more accurate and applicable than the DEG set. Besides, p-STAT3 and IL-31 showing correlation to immune infiltration of ovarian cancer tissues may be potential therapeutic targets for treating ovarian cancers.

Deconvolution of the Genomic and Epigenomic Interaction Landscape of Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is the most aggressive form of breast cancer. Emerging evidenced suggests that both genetics and epigenetic factors play a role in the pathogenesis of TNBC. However, oncogenic interactions and cooperation between genomic and epigenomic variation have not been characterized. The objective of this study was to deconvolute the genomic and epigenomic interaction landscape in TNBC using an integrative genomics approach, which integrates information on germline, somatic, epigenomic and gene expression variation. We hypothesized that TNBC originates from a complex interplay between genomic (both germline and somatic variation) and epigenomic variation. We further hypothesized that these complex arrays of interacting genomic and epigenomic factors affect entire molecular networks and signaling pathways which, in turn, drive TNBC. We addressed these hypotheses using germline variation from genome-wide association studies and somatic, epigenomic and gene expression variation from The Cancer Genome Atlas (TCGA). The investigation revealed signatures of functionally related genes containing germline, somatic and epigenetic variations. DNA methylation had an effect on gene expression. Network and pathway analysis revealed molecule networks and signaling pathways enriched for germline, somatic and epigenomic variation, among them: Role of BRCA1 in DNA Damage Response, Hereditary Breast Cancer Signaling, Molecular Mechanisms of Cancer, Estrogen-Dependent Breast Cancer, p53, MYC Mediated Apoptosis, and PTEN Signaling pathways. The investigation revealed that integrative genomics is a powerful approach for deconvoluting the genomic-epigenomic interaction landscape in TNBC. Further studies are needed to understand the biological mechanisms underlying oncogenic interactions between genomic and epigenomic factors in TNBC.

Combining DNA methylation and RNA sequencing data of cancer for supervised knowledge extraction

Background

In the Next Generation Sequencing (NGS) era a large amount of biological data is being sequenced, analyzed, and stored in many public databases, whose interoperability is often required to allow an enhanced accessibility. The combination of heterogeneous NGS genomic data is an open challenge: the analysis of data from different experiments is a fundamental practice for the study of diseases. In this work, we propose to combine DNA methylation and RNA sequencing NGS experiments at gene level for supervised knowledge extraction in cancer.

Methods

We retrieve DNA methylation and RNA sequencing datasets from The Cancer Genome Atlas (TCGA), focusing on the Breast Invasive Carcinoma (BRCA), the Thyroid Carcinoma (THCA), and the Kidney Renal Papillary Cell Carcinoma (KIRP). We combine the RNA sequencing gene expression values with the gene methylation quantity, as a new measure that we define for representing the methylation quantity associated to a gene. Additionally, we propose to analyze the combined data through tree- and rule-based classification algorithms (C4.5, Random Forest, RIPPER, and CAMUR).

Results

We extract more than 15,000 classification models (composed of gene sets), which allow to distinguish the tumoral samples from the normal ones with an average accuracy of 95%. From the integrated experiments we obtain about 5000 classification models that consider both the gene measures related to the RNA sequencing and the DNA methylation experiments.

Conclusions

We compare the sets of genes obtained from the classifications on RNA sequencing and DNA methylation data with the genes obtained from the integration of the two experiments. The comparison results in several genes that are in common among the single experiments and the integrated ones (733 for BRCA, 35 for KIRP, and 861 for THCA) and 509 genes that are in common among the different experiments. Finally, we investigate the possible relationships among the different analyzed tumors by extracting a core set of 13 genes that appear in all tumors. A preliminary functional analysis confirms the relation of part of those genes (5 out of 13 and 279 out of 509) with cancer, suggesting to focus further studies on the new individuated ones.

Electronic supplementary material

The online version of this article (10.1186/s13040-018-0184-6) contains supplementary material, which is available to authorized users.

Convergence of evidence from a methylome-wide CpG-SNP association study and GWAS of major depressive disorder

DNA methylation is an epigenetic modification that provides stability and diversity to the cellular phenotype. It is influenced by both genetic sequence variation and environmental factors, and can therefore potentially account for variation of heritable phenotypes and disorders. Therefore, methylome-wide association studies (MWAS) are promising complements to genome-wide association studies (GWAS) of genetic variants. Of particular interest are methylation sites (CpGs) that are created or destroyed by the alleles of single-nucleotide polymorphisms (SNPs), as these so-called CpG-SNPs may show variation in methylation levels on top of what can be explained by the sequence variation. Using sequencing-based data from 1132 major depressive disorder (MDD) cases and controls, we performed a MWAS of 970,414 common CpG-SNPs. The analysis identified 27 suggestively significant (P < 1.00 × 10^-5) CpG-SNPs associations. Furthermore, the MWAS results were over-represented (odds ratios ranging 1.36-5.00; P ranging 4.9 × 10^-3-8.1 × 10^-2) among findings from three recent GWAS for MDD-related phenotypes. Overlapping loci included, e.g., ROBO2, ASIC2, and DCC. As the CpG-SNP analysis accounts for the number of alleles that creates CpGs, the methylation differences could not be explained by differences in allele frequencies. Thus, the results show that the MWAS and GWASs provide independent lines of evidence for the involvement of these loci in MDD. In conclusion, our methylation study of MDD contributes novel information about loci of relevance that complements previous findings and generates new hypothesis about MDD etiology, such as that the functional effects of genetic association may be partly mediated and/or enhanced by the methylation status in these loci.

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.

Evaluation of genetic variants in autophagy pathway genes as prognostic biomarkers for breast cancer

Autophagy-related genes (ATGs) play a critical role in the development of various diseases including cancer. However, the role of ATGs in breast cancer survival remains unclear. This study aims to investigate whether genetic variants in core ATGs are correlated with the prognosis of breast cancer. A total of 14 potentially functional variants in core ATGs were genotyped in 790 breast cancer patients. The association of each variant with breast cancer-specific survival was evaluated by log-rank test and Cox regression model. In silico analysis was also performed to evaluate the potential function of selected variants. We found that one variant in ATG7 rs8154 (A>G) was significantly associated with breast cancer-specific survival after adjusted for age and clinical stage (HR=1.61, 95% CI: 1.12-2.31, P=0.010). Stratified analysis showed that the prognostic role of rs8154 was significant in subgroups of elder age, elder menarche age, and postmenopausal status (all P<0.001). Interaction effects were also detected between rs8154 and these grouping variables. In silico analysis revealed that rs8154 was annotated to be expression quantitative trait loci (eQTL) and methylation quantitative trait loci (meQTL) based on Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) datasets (both P<0.05). In addition, upregulated expression of ATG7 in breast cancer tissues was observed and is significantly associated with poorer overall survival (log-rank P=0.015), poorer relapse free survival (log-rank P=0.017), and poorer distant metastasis free survival (log-rank P=0.034) in different datasets. Summarily, ATG7 variant rs8154 represents a novel prognostic marker for breast cancer patients, which may shed light on clinical risk stratification and therapeutic decision making.

Prognostic value of routine laboratory variables in prediction of breast cancer recurrence

The prognostic value of routine laboratory variables in breast cancer has been largely overlooked. Based on laboratory tests commonly performed in clinical practice, we aimed to develop a new model to predict disease free survival (DFS) after surgical removal of primary breast cancer. In a cohort of 1,596 breast cancer patients, we analyzed the associations of 33 laboratory variables with patient DFS. Based on 3 significant laboratory variables (hemoglobin, alkaline phosphatase, and international normalized ratio), together with important demographic and clinical variables, we developed a prognostic model, achieving the area under the curve of 0.79. We categorized patients into 3 risk groups according to the prognostic index developed from the final model. Compared with the patients in the low-risk group, those in the medium- and high-risk group had a significantly increased risk of recurrence with a hazard ratio (HR) of 1.75 (95% confidence interval [CI] 1.30–2.38) and 4.66 (95% CI 3.54–6.14), respectively. The results from the training set were validated in the testing set. Overall, our prognostic model incorporating readily available routine laboratory tests is powerful in identifying breast cancer patients who are at high risk of recurrence. Further study is warranted to validate its clinical application.