Methylation of NBPF1 as a novel marker for the detection of plasma cell-free DNA of breast cancer patients

Methylation signatures as biomarkers for non-invasive early detection of breast cancer: A systematic review of the literature

BACKGROUND

Early detection of breast cancer would help alleviate the burden of treatment for early-stage breast cancer and help patient prognosis. There is currently no established gene panel that utilizes the potential of DNA methylation as a molecular signature for the early detection of breast cancer. This systematic review aims to identify the optimal methylation biomarkers for a non-invasive liquid biopsy assay and the gaps in knowledge regarding biomarkers for early detection of breast cancer.

METHODS

Following the PRISMA-ScR method, Pubmed and Google Scholar was searched for publications related to methylation biomarkers in breast cancer over a five-year period. Eligible publications were mined for key data fields such as study aims, cohort demographics, types of breast cancer studied, technologies used, and outcomes. Data was analyzed to address the objectives of the review.

RESULTS

Literature search identified 112 studies of which based on eligibility criteria, 13 studies were included. 28 potential methylation gene targets were identified, of which 23 were methylated at the promoter region, 1 was methylated in the body of the gene and 4 were methylated at yet to be identified locations.

CONCLUSIONS

Our evaluation shows that at minimum APC, RASSFI, and FOXA1 genes would be a promising set of genes to start with for the early detection of breast cancer, based on the sensitivity and specificity outlined in the studies. Prospective studies are needed to optimize biomarkers for broader impact in early detection of breast cancer.

Potential applications of DNA methylation testing technology in female tumors and screening methods

DNA methylation is a common epigenetic modification, and the current commonly used methods for DNA methylation detection include methylation-specific PCR, methylation-sensitive restriction endonuclease-PCR, and methylation-specific sequencing. DNA methylation plays an important role in genomic and epigenomic studies, and combining DNA methylation with other epigenetic modifications, such as histone modifications, may lead to better DNA methylation. DNA methylation also plays an important role in the development of disease, and analyzing changes in individual DNA methylation patterns can provide individualized diagnostic and therapeutic solutions. Liquid biopsy techniques are also increasingly well established in clinical practice and may provide new methods for early cancer screening. It is important to find new screening methods that are easy to perform, minimally invasive, patient-friendly, and affordable. DNA methylation mechanisms are thought to have an important role in cancer and have potential applications in the diagnosis and treatment of female tumors. This review discussed early detection targets and screening methods for common female tumors such as breast, ovarian, and cervical cancers and discussed advances in the study of DNA methylation in these tumors. Although existing screening, diagnostic, and treatment modalities exist, the high morbidity and mortality rates of these tumors remain challenging.

The mutational landscape of a US Midwestern breast cancer cohort reveals subtype-specific cancer drivers and prognostic markers

BACKGROUND

Female breast cancer remains the second leading cause of cancer-related death in the USA. The heterogeneity in the tumor morphology across the cohort and within patients can lead to unpredictable therapy resistance, metastasis, and clinical outcome. Hence, supplementing classic pathological markers with intrinsic tumor molecular markers can help identify novel molecular subtypes and the discovery of actionable biomarkers.

METHODS

We conducted a large multi-institutional genomic analysis of paired normal and tumor samples from breast cancer patients to profile the complex genomic architecture of breast tumors. Long-term patient follow-up, therapeutic regimens, and treatment response for this cohort are documented using the Breast Cancer Collaborative Registry. The majority of the patients in this study were at tumor stage 1 (51.4%) and stage 2 (36.3%) at the time of diagnosis. Whole-exome sequencing data from 554 patients were used for mutational profiling and identifying cancer drivers.

RESULTS

We identified 54 tumors having at least 1000 mutations and 185 tumors with less than 100 mutations. Tumor mutational burden varied across the classified subtypes, and the top ten mutated genes include MUC4, MUC16, PIK3CA, TTN, TP53, NBPF10, NBPF1, CDC27, AHNAK2, and MUC2. Patients were classified based on seven biological and tumor-specific parameters, including grade, stage, hormone receptor status, histological subtype, Ki67 expression, lymph node status, race, and mutational profiles compared across different subtypes. Mutual exclusion of mutations in PIK3CA and TP53 was pronounced across different tumor grades. Cancer drivers specific to each subtype include TP53, PIK3CA, CDC27, CDH1, STK39, CBFB, MAP3K1, and GATA3, and mutations associated with patient survival were identified in our cohort.

CONCLUSIONS

This extensive study has revealed tumor burden, driver genes, co-occurrence, mutual exclusivity, and survival effects of mutations on a US Midwestern breast cancer cohort, paving the way for developing personalized therapeutic strategies.

A senescence-related signature for predicting the prognosis of breast cancer: A bioinformatics analysis

Breast cancer is a heterogeneous disease with diverse prognosis and treatment outcomes. Current gene signatures for prognostic prediction are limited to specific subtypes of breast cancer. Cellular senescence is a state of irreversible cell cycle arrest that affects various physiological and pathological processes. This study aimed to develop and validate a senescence-related signature for predicting the prognosis of breast cancer patients. We retrieved 744 senescence-associated genes from the SeneQuest database and analyzed their expression profiles in 2 large datasets of breast cancer patients: The Cancer Genome Atlas (TCGA) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC). We used univariate Cox regression analysis, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox regression analysis to derive a 29-gene senescence-related risk signature. The risk signature was significantly associated with disease-specific survival (DSS), clinical characteristics, molecular subtypes, and immune checkpoint genes expressions in both datasets. The risk signature also stratified high-risk and low-risk patients within the same clinical stage and molecular subtype. The risk signature was an independent prognostic factor for breast cancer patients. The senescence-related signature may be a useful biomarker for predicting prognosis and immunotherapy response of breast cancer patients. The risk signature may also guide adjuvant chemotherapy decisions, especially in hormone receptor positive (HR+) and human epidermal growth factor receptor type 2 (HER2)- subtypes.

Characteristic gene prognostic model of type 1 diabetes mellitus via machine learning strategy

The present study was designed to detect possible biomarkers associated with Type 1 diabetes mellitus (T1DM) incidence in an effort to develop novel treatments for this condition. Three mRNA expression datasets of peripheral blood mononuclear cells (PBMCs) were obtained from the GEO database. Differentially expressed genes (DEGs) between T1DM patients and healthy controls were identified by Limma package in R, and using the DEGs to conduct GO and DO pathway enrichment. The LASSO-SVM were used to screen the hub genes. We performed immune correlation analysis of hub genes and established a T1DM prognosis model. CIBERSORT algorithm was used to identify the different immune cells in distribution between T1DM and normal samples. The correlation of the hub genes and immune cells was analyzed by Spearman. ROC curves were used to assess the diagnostic value of genes in T1DM. A total of 60 immune related DEGs were obtained from the T1DM and normal samples. Then, DEGs were further screened to obtain 3 hub genes, ANP32A-IT1, ESCO2 and NBPF1. CIBERSORT analysis revealed the percentage of immune cells in each sample, indicating that there was significant difference in monocytes, T cells CD8+, gamma delta T cells, naive CD4+ T cells and activated memory CD4+ T cells between T1DM and normal samples. The area under curve (AUC) of ESCO2, ANP32A-IT1 and NBPF1 were all greater than 0.8, indicating that these three genes have high diagnostic value for T1DM. Together, the findings of these bioinformatics analyses thus identified key hub genes associated with T1DM development.

The assessment of circulating cell-free DNA as a diagnostic tool for breast cancer: an updated systematic review and meta-analysis of quantitative and qualitative ssays

OBJECTIVES

This updated meta-analysis aimed to assess the diagnostic accuracy of circulating cell-free DNA (cfDNA) in breast cancer (BC).

CONTENT

An extensive systematic search was performed in PubMed, Scopus, Embase, and Science Direct databases to retrieve all related literature. Various diagnostic estimates, including sensitivity (SE), specificity (SP), likelihood ratios (LRs), diagnostic odds ratio (DOR), and area under the curve (AUC) of summary receiver operating characteristic (sROC) curve, were also calculated using bivariate linear mixed models.

SUMMARY

In this meta-analysis, 57 unique articles (130 assays) on 4246 BC patients and 2,952 controls, were enrolled. For quantitative approaches, pooled SE, SP, PLR, NLR, DOR, and AUC were obtained as 0.80, 0.88, 6.7, 0.23, 29, and 0.91, respectively. Moreover, for qualitative approaches, pooled SE and SP for diagnostic performance were obtained as 0.36 and 0.98, respectively. In addition, PLR was 14.9 and NLR was 0.66. As well, the combined DOR was 23, and the AUC was 0.79.

OUTLOOK

Regardless of promising SE and SP, analysis of LRs suggested that quantitative assays are not robust enough neither for BC confirmation nor for its exclusion. On the other hand, qualitative assays showed satisfying performance only for confirming the diagnosis of BC, but not for its exclusion. Furthermore, qualitative cfDNA assays showed a better diagnostic performance in patients at the advanced stage of cancer, which represented no remarkable clinical significance as a biomarker for early detection.

A review on methods for diagnosis of breast cancer cells and tissues

Breast cancer has seriously been threatening physical and mental health of women in the world, and its morbidity and mortality also show clearly upward trend in China over time. Through inquiry, we find that survival rate of patients with early‐stage breast cancer is significantly higher than those with middle‐ and late‐stage breast cancer, hence, it is essential to conduct research to quickly diagnose breast cancer. Until now, many methods for diagnosing breast cancer have been developed, mainly based on imaging and molecular biotechnology examination. These methods have great contributions in screening and confirmation of breast cancer. In this review article, we introduce and elaborate the advances of these methods, and then conclude some gold standard diagnostic methods for certain breast cancer patients. We lastly discuss how to choose the most suitable diagnostic methods for breast cancer patients. In general, this article not only summarizes application and development of these diagnostic methods, but also provides the guidance for researchers who work on diagnosis of breast cancer.

Cell free DNA biology and its involvement in breast carcinogenesis

Liquid biopsy represents a procedure for minimally invasive analysis of non-solid tissue, blood and other body fluids. It comprises a set of analytes that includes circulating tumor cells (CTCs) and circulating free DNA (cfDNA), RNA, long noncoding RNA (lncRNA) and micro RNA (miRNA), as well as extracellular vesicles. These novel analytes represent an alternative tool to complement diagnosis and monitor and predict response to treatment of the tumoral process and may be used for other disease processes such viral and parasitic infection. This review focuses on the biologic and molecular characteristics of cfDNA in general and the molecular changes (mutational and epigenetic) proven useful in oncologic practice for diagnosis, monitoring and treatment of breast cancer specifically.

DNA Methylation-Based Testing in Liquid Biopsies as Detection and Prognostic Biomarkers for the Four Major Cancer Types

Lung, breast, colorectal, and prostate cancers are the most incident worldwide. Optimal population-based cancer screening methods remain an unmet need, since cancer detection at early stages increases the prospects of successful and curative treatment, leading to a lower incidence of recurrences. Moreover, the current parameters for cancer patients’ stratification have been associated with divergent outcomes. Therefore, new biomarkers that could aid in cancer detection and prognosis, preferably detected by minimally invasive methods are of major importance. Aberrant DNA methylation is an early event in cancer development and may be detected in circulating cell-free DNA (ccfDNA), constituting a valuable cancer biomarker. Furthermore, DNA methylation is a stable alteration that can be easily and rapidly quantified by methylation-specific PCR methods. Thus, the main goal of this review is to provide an overview of the most important studies that report methylation biomarkers for the detection and prognosis of the four major cancers after a critical analysis of the available literature. DNA methylation-based biomarkers show promise for cancer detection and management, with some studies describing a “PanCancer” detection approach for the simultaneous detection of several cancer types. Nonetheless, DNA methylation biomarkers still lack large-scale validation, precluding implementation in clinical practice.

Cell-Free DNA Methylation Profiling Analysis-Technologies and Bioinformatics

Analysis of circulating nucleic acids in bodily fluids, referred to as “liquid biopsies”, is rapidly gaining prominence. Studies have shown that cell-free DNA (cfDNA) has great potential in characterizing tumor status and heterogeneity, as well as the response to therapy and tumor recurrence. DNA methylation is an epigenetic modification that plays an important role in a broad range of biological processes and diseases. It is well known that aberrant DNA methylation is generalizable across various samples and occurs early during the pathogenesis of cancer. Methylation patterns of cfDNA are also consistent with their originated cells or tissues. Systemic analysis of cfDNA methylation profiles has emerged as a promising approach for cancer detection and origin determination. In this review, we will summarize the technologies for DNA methylation analysis and discuss their feasibility for liquid biopsy applications. We will also provide a brief overview of the bioinformatic approaches for analysis of DNA methylation sequencing data. Overall, this review provides informative guidance for the selection of experimental and computational methods in cfDNA methylation-based studies.

Gene expression alterations of human liver cancer cells following borax exposure

Borax is a boron compound that is becoming widely recognized for its biological effects, including lipid peroxidation, cytotoxicity, genotoxicity, antioxidant activity and potential therapeutic benefits. However, it remains unknown whether exposure of human liver cancer (HepG2) cells to borax affects the gene expression of these cells. HepG2 cells were treated with 4 mM borax for either 2 or 24 h. Gene expression analysis was performed using Affymetrix GeneChip Human Gene 2.0 ST Arrays, which was followed by gene ontology analysis and pathway analysis. The clustering result was validated using reverse transcription-quantitative polymerase chain reaction. A cell proliferation assay was performed using Celigo Image Cytometer Instrumentation. Following this, 2- or 24-h exposure to borax significantly altered the expression level of a number of genes in HepG2 cells, specifically 530 genes (384 upregulated and 146 downregulated) or 1,763 genes (1,044 upregulated and 719 downregulated) compared with the control group, respectively (≥2-fold; P<0.05). Twenty downregulated genes were abundantly expressed in HepG2 cells under normal conditions. Furthermore, the growth of HepG2 cells was inhibited through the downregulation of PRUNE1, NBPF1, PPcaspase-1, UPF2 and MBTPS1 (≥1.5-fold, P<0.05). The dysregulated genes potentially serve important roles in various biological processes, including the inflammation response, stress response, cellular growth, proliferation, apoptosis and tumorigenesis/oncolysis.

Prognostic value of circulating cell-free DNA in patients with pancreatic cancer: A systemic review and meta-analysis

Because of the deep research about tumorigenesis mechanism, the cognition of cancer has been transferred to molecular level from morphology. Previous articles reported a potential connection between circulating cell-free DNA (cfDNA) and prognosis of pancreatic cancer. A total of 18 related articles including 1243 patients were enrolled to access the relationship between cfDNA and prognosis of pancreatic cancer. The hazard ratio (HR) was used to combine the univariate and multivariate results of included studies. Our result performed that the cfDNA had significant prognostic value in predicting OS (HR = 2.41, 95%CI: 1.93-3.02, I2 = 60%) and PFS (HR = 2.47, 95%CI: 1.80-3.40, I2 = 0%) in univariate analysis. The multivariate analyses about OS (HR = 2.57, 95%CI: 1.95-3.38, I2 = 66%) and PFS (HR = 2.31, 95%CI: 1.47-3.64, I2 = 0%) also showed significance. In conclusion, the cfDNA was a significant prognostic factor for OS and PFS in patients with pancreatic cancer. The mutation (Kras, ERBB2-exon17 and KrasG12V), circulating tumor DNA (ctDNA) presence, hypermethylation and higher concentration of cfDNA were both associated with worse survival results in pancreatic cancer.