Identification of Biomarkers for Predicting Lymph Node Metastasis of Stomach Cancer Using Clinical DNA Methylation Data. Dis Markers. 2017;2017:5745724. [PMID: 28951630 PMCID: PMC5603126 DOI: 10.1155/2017/5745724]

Methods in DNA methylation array dataset analysis: A review

Understanding the intricate relationships between gene expression levels and epigenetic modifications in a genome is crucial to comprehending the pathogenic mechanisms of many diseases. With the advancement of DNA Methylome Profiling techniques, the emphasis on identifying Differentially Methylated Regions (DMRs/DMGs) has become crucial for biomarker discovery, offering new insights into the etiology of illnesses. This review surveys the current state of computational tools/algorithms for the analysis of microarray-based DNA methylation profiling datasets, focusing on key concepts underlying the diagnostic/prognostic CpG site extraction. It addresses methodological frameworks, algorithms, and pipelines employed by various authors, serving as a roadmap to address challenges and understand changing trends in the methodologies for analyzing array-based DNA methylation profiling datasets derived from diseased genomes. Additionally, it highlights the importance of integrating gene expression and methylation datasets for accurate biomarker identification, explores prognostic prediction models, and discusses molecular subtyping for disease classification. The review also emphasizes the contributions of machine learning, neural networks, and data mining to enhance diagnostic workflow development, thereby improving accuracy, precision, and robustness.

A review on trends in development and translation of omics signatures in cancer

The field of cancer genomics and transcriptomics has evolved from targeted profiling to swift sequencing of individual tumor genome and transcriptome. The steady growth in genome, epigenome, and transcriptome datasets on a genome-wide scale has significantly increased our capability in capturing signatures that represent both the intrinsic and extrinsic biological features of tumors. These biological differences can help in precise molecular subtyping of cancer, predicting tumor progression, metastatic potential, and resistance to therapeutic agents. In this review, we summarized the current development of genomic, methylomic, transcriptomic, proteomic and metabolic signatures in the field of cancer research and highlighted their potentials in clinical applications to improve diagnosis, prognosis, and treatment decision in cancer patients.

Evaluation of a three-gene methylation model for correlating lymph node metastasis in postoperative early gastric cancer adjacent samples

Background

Lymph node metastasis (LNM) has a profound impact on the treatment and prognosis of early gastric cancer (EGC), yet the existing evaluation methods lack accuracy. Recent research has underscored the role of precancerous lesions in tumor progression and metastasis. The objective of this study was to utilize the previously developed EGC LNM prediction model to further validate and extend the analysis in paired adjacent tissue samples.

Methods

We evaluated the model in a monocentric study using Methylight, a methylation-specific PCR technique, on postoperative fresh-frozen EGC samples (n = 129) and paired adjacent tissue samples (n = 129).

Results

The three-gene methylation model demonstrated remarkable efficacy in both EGC and adjacent tissues. The model demonstrated excellent performance, with areas under the curve (AUC) of 0.85 and 0.82, specificities of 85.1% and 80.5%, sensitivities of 83.3% and 73.8%, and accuracies of 84.5% and 78.3%, respectively. It is noteworthy that the model demonstrated superior performance compared to computed tomography (CT) imaging in the adjacent tissue group, with an area under the curve (AUC) of 0.86 compared to 0.64 (p < 0.001). Furthermore, the model demonstrated superior diagnostic capability in these adjacent tissues (AUC = 0.82) compared to traditional clinicopathological features, including ulceration (AUC = 0.65), invasional depth (AUC = 0.66), and lymphovascular invasion (AUC = 0.69). Additionally, it surpassed traditional models based on these features (AUC = 0.77).

Conclusion

The three-gene methylation prediction model for EGC LNM is highly effective in both cancerous and adjacent tissue samples in a postoperative setting, providing reliable diagnostic information. This extends its clinical utility, particularly when tumor samples are scarce, making it a valuable tool for evaluating LNM status and assisting in treatment planning.

Identification of molecular characteristics of FUT8 and alteration of core fucosylation in kidney renal clear cell cancer

BACKGROUND

Kidney renal clear cell cancer (KIRC) is a type of urological cancer that occurs worldwide. Core fucosylation (CF), as the most common post-translational modification, is involved in the tumorigenesis.

METHODS

The alterations of CF-related genes were summarized in pan-cancer. The "ConsensusClusterPlus" package was utilized to identify two CF-related KIRC subtypes. The "ssgsea" function was chosen to estimate the CF score, signaling pathways and cell deaths. Multiple algorithms were applied to assess immune responses. The "oncoPredict" was utilized to estimate the drug sensitivity. The IHC and subgroup analysis was performed to reveal the molecular features of FUT8. Single-cell RNA sequencing (scRNA-seq) data were scrutinized to evaluate the CF state.

RESULTS

In pan-cancer, there was a noticeable alteration in the expression of CF-related genes. In KIRC, two CF-related subtypes (i.e., C1, C2) were obtained. In comparison to C2, C1 exhibited a higher CF score and correlated with poorer overall survival. Additionally, the TME of C2 demonstrated increased activity in neutrophils, macrophages, myeloid dendritic cells, and B cells, alongside a higher presence of silent mast cells, NK cells, and endothelial cells. Compared to normal samples, higher expression of FUT8 is observed in KIRC. The mutation of SETD2 was more frequent in low-FUT8 samples while the mutation of DNAH9 was more frequent in high-FUT8 samples. scRNA-seq analyses revealed that the CF score was predominantly higher in endothelial cells and fibroblast cells.

CONCLUSIONS

Two CF-related subtypes with distinct prognosis and TME were identified in KIRC. FUT8 exhibited elevated expression in KIRC samples.

Serum-based Comprehensive N-Glycans Profiling Analysis in Different Gastric Disease Stages by Porous Graphitic Carbon Liquid Chromatography-Mass Spectrometry Associated With Potential Marker Discovery

BACKGROUND/AIM

N-glycans are potential serum biomarkers due to their aberrant structure and abundance alteration during disease progression. Few studies have been associated with relative quantitative N-glycans profiling during different gastric disease stages. In this study, we conducted an investigation on the profiling of N-glycans in patients with gastric disease, as well as in healthy controls.

MATERIALS AND METHODS

In this study, the porous graphitization carbon chromatography-high resolution Fourier transform mass spectrometry (PGC-FTMS) method was applied to assess comprehensive N-glycans profiling in patients at different stages of gastric disease, including gastritis, atrophic gastritis, gastric ulcer, gastric polyps, and gastric cancer.

RESULTS

A total of 45 N-glycans (relative abundance >0.1%) were detected, and 9 N-glycans were found to be potential biomarkers for gastric disease detection. Along with the progression of gastric disease, the abundance of sialylated N-glycans increased, while that of core-fucosylated N-glycans decreased. Multivariate statistical analysis demonstrated that N-glycans profiling between gastritis and healthy controls had significant differences. The characteristic N-glycans distinguished gastric cancer from healthy controls, which had strong clinical diagnostic value.

CONCLUSION

The relative quantitative profile of N-glycans in different gastric disease stages was revealed and serum N-glycans are proposed for distinguishing gastric disease stages in clinical application.

A Heuristic Machine Learning-Based Optimization Technique to Predict Lung Cancer Patient Survival

Cancer has been a significant threat to human health and well-being, posing the biggest obstacle in the history of human sickness. The high death rate in cancer patients is primarily due to the complexity of the disease and the wide range of clinical outcomes. Increasing the accuracy of the prediction is equally crucial as predicting the survival rate of cancer patients, which has become a key issue of cancer research. Many models have been suggested at the moment. However, most of them simply use single genetic data or clinical data to construct prediction models for cancer survival. There is a lot of emphasis in present survival studies on determining whether or not a patient will survive five years. The personal issue of how long a lung cancer patient will survive remains unanswered. The proposed technique Naive Bayes and SSA is estimating the overall survival time with lung cancer. Two machine learning challenges are derived from a single customized query. To begin with, determining whether a patient will survive for more than five years is a simple binary question. The second step is to develop a five-year survival model using regression analysis. When asked to forecast how long a lung cancer patient would survive within five years, the mean absolute error (MAE) of this technique's predictions is accurate within a month. Several biomarker genes have been associated with lung cancers. The accuracy, recall, and precision achieved from this algorithm are 98.78%, 98.4%, and 98.6%, respectively.

Identification and validation of DNA methylation markers to predict axillary lymph node metastasis of breast cancer

BACKGROUND

Axillary lymph node metastasis (ALNM) is one of the most important prognostic factors for breast cancer patients, and DNA methylation is involved in ALNM of breast cancer. However, the methylation profile of breast cancer ALNM remains unknown.

METHODS

Breast cancer tissues were collected from patients with and without ALNM. We investigated the genome-wide DNA methylation profile in breast cancer with and without ALNM using reduced representation bisulfite sequencing (RRBS). Then, differentially methylated regions (DMRs) were verified by targeted bisulfite sequencing.

RESULTS

A total of 21491 DMRs were identified between the lymph node positive group and negative group. Compared to the LN-negative breast cancer, LN-positive breast cancer had 10,920 hypermethylated DMRs and 10,571 hypomethylated DMRs. Then, 10 DMRs in the gene promoter region were detected by targeted bisulfite sequencing, these gene included HOXA5, PTOV1-AS1, RHOF, PAX6, GSTP1, RASGRF2, AKR1B1, BNIP3, CRMP1, ING5. Compared with negative lymph node, the promoter methylation levels of RASGRF2, AKR1B1 and CRMP1 increased in positive lymph node, while the promoter methylation level of RHOF decreased in positive lymph node. In addition, Cancer Genome Atlas (TCGA) data showed that RASGRF2, AKR1B1 and CRMP1 were low expressed in breast Cancer tissues, while RHOF was high expressed in breast Cancer tissues. Furthermore, in addition to highly methylated AKR1B1, RASGRF2 and CRMP1 gene promoters, BNIP3, GSTP1, HOXA5 and PAX6 gene promoters were also methylated in ER-positive and HER2-negative breast cancer with ALNM.

CONCLUSIONS

When compared to negative lymph node breast cancer, the positive lymph node breast cancer has a differential methylation status. Promoter methylation of RASGRF2, AKR1B1, CRMP1 and RHOF in lymph node positive breast cancer tissues was significantly different from that in lymph node negative breast cancer tissues. AKR1B1, RASGRF2, CRMP1, BNIP3, GSTP1, HOXA5 and PAX6 genes were methylated in ER-positive and HER2-negative breast cancer with ALNM. The study provides an important biological base for understanding breast cancer with ALNM and developing therapeutic targets for breast cancer with ALNM.

Genome-wide methylation profiling identify hypermethylated HOXL subclass genes as potential markers for esophageal squamous cell carcinoma detection

BACKGROUND

Numerous studies have revealed aberrant DNA methylation in esophageal squamous cell carcinoma (ESCC). However, they often focused on the partial genome, which resulted in an inadequate understanding of the shaped methylation features and the lack of available methylation markers for this disease.

METHODS

The current study investigated the methylation profiles between ESCC and paired normal samples using whole-genome bisulfite sequencing (WGBS) data and obtained a group of differentially methylated CpGs (DMC), differentially methylated regions (DMR), and differentially methylated genes (DMG). The DMGs were then verified in independent datasets and Sanger sequencing in our custom samples. Finally, we attempted to evaluate the performance of these genes as methylation markers for the classification of ESCC.

RESULTS

We obtained 438,558 DMCs, 15,462 DMRs, and 1568 DMGs. The four significantly enriched gene families of DMGs were CD molecules, NKL subclass, HOXL subclass, and Zinc finger C2H2-type. The HOXL subclass homeobox genes were observed extensively hypermethylated in ESCC. The HOXL-score estimated by HOXC10 and HOXD1 methylation, whose methylation status were then confirmed by sanger sequencing in our custom ESCC samples, showed good ability in discriminating ESCC from normal samples.

CONCLUSIONS

We observed widespread hypomethylation events in ESCC, and the hypermethylated HOXL subclass homeobox genes presented promising applications for the early detection of esophageal squamous cell carcinoma.

Significance of HOXD transcription factors family in progression, migration and angiogenesis of cancer

The transcription factors (TFs) of the HOX family play significant roles during early embryonic development and cellular processes. They also play a key role in tumorigenesis as tumor oncogenes or suppressors. Furthermore, TFs of the HOXD geFIne cluster affect proliferation, migration, and invasion of tumors. Consequently, dysregulated activity of HOXD TFs has been linked to clinicopathological characteristics of cancer. HOXD TFs are regulated by non-coding RNAs and methylation of DNA on promoter and enhancer regions. In addition, HOXD genes modulate the biological function of cancer cells via the MEK and AKT signaling pathways, thus, making HOXD TFs, a suitable molecular marker for cancer prognosis and therapy. In this review, we summarized the roles of HOXD TFs in different cancers and highlighted its potential as a diagnostic and therapeutic target.

Development and Validation of Machine Learning Models for Predicting Occult Nodal Metastasis in Early-Stage Oral Cavity Squamous Cell Carcinoma

IMPORTANCE

Given that early-stage oral cavity squamous cell carcinoma (OCSCC) has a high propensity for subclinical nodal metastasis, elective neck dissection has become standard practice for many patients with clinically negative nodes. Unfortunately, for most patients without regional metastasis, this risk-averse treatment paradigm results in unnecessary morbidity.

OBJECTIVES

To develop and validate predictive models of occult nodal metastasis from clinicopathological variables that were available after surgical extirpation of the primary tumor and to compare predictive performance against depth of invasion (DOI), the currently accepted standard.

DESIGN, SETTING, AND PARTICIPANTS

This diagnostic modeling study collected clinicopathological variables retrospectively from 7 tertiary care academic medical centers across the US. Participants included adult patients with early-stage OCSCC without nodal involvement who underwent primary surgical extirpation with or without upfront elective neck dissection. These patients were initially evaluated between January 1, 2000, and December 31, 2019.

EXPOSURES

Largest tumor dimension, tumor thickness, DOI, margin status, lymphovascular invasion, perineural invasion, muscle invasion, submucosal invasion, dysplasia, histological grade, anatomical subsite, age, sex, smoking history, race and ethnicity, and body mass index (calculated as weight in kilograms divided by height in meters squared).

MAIN OUTCOMES AND MEASURES

Occult nodal metastasis identified either at the time of elective neck dissection or regional recurrence within 2 years of initial surgery.

RESULTS

Of the 634 included patients (mean [SD] age, 61.2 [13.6] years; 344 men [54.3%]), 114 (18.0%) had occult nodal metastasis. Patients with occult nodal metastasis had a higher frequency of lymphovascular invasion (26.3% vs 8.1%; P < .001), perineural invasion (40.4% vs 18.5%; P < .001), and margin involvement by invasive tumor (12.3% vs 6.3%; P = .046) compared with those without pathological lymph node metastasis. In addition, patients with vs those without occult nodal metastasis had a higher frequency of poorly differentiated primary tumor (20.2% vs 6.2%; P < .001) and greater DOI (7.0 vs 5.4 mm; P < .001). A predictive model that was built with XGBoost architecture outperformed the commonly used DOI threshold of 4 mm, achieving an area under the curve of 0.84 (95% CI, 0.80-0.88) vs 0.62 (95% CI, 0.57-0.67) with DOI. This model had a sensitivity of 91.7%, specificity of 72.6%, positive predictive value of 39.3%, and negative predictive value of 97.8%.

CONCLUSIONS AND RELEVANCE

Results of this study showed that machine learning models that were developed from multi-institutional clinicopathological data have the potential to not only reduce the number of pathologically node-negative neck dissections but also accurately identify patients with early OCSCC who are at highest risk for nodal metastases.

A novel DNA methylation signature associated with lymph node metastasis status in early gastric cancer

Background

Lymph node metastasis (LNM) is an important factor for both treatment and prognosis of early gastric cancer (EGC). Current methods are insufficient to evaluate LNM in EGC due to suboptimal accuracy. Herein, we aim to identify methylation signatures for LNM of EGC, facilitate precision diagnosis, and guide treatment modalities.

Methods

For marker discovery, genome-wide methylation sequencing was performed in a cohort (marker discovery) using 47 fresh frozen (FF) tissue samples. The identified signatures were subsequently characterized for model development using formalin-fixed paraffin-embedded (FFPE) samples by qPCR assay in a second cohort (model development cohort, n = 302, training set: n = 151, test set: n = 151). The performance of the established model was further validated using FFPE samples in a third cohorts (validation cohort, n = 130) and compared with image-based diagnostics, conventional clinicopathology-based model (conventional model), and current standard workups.

Results

Fifty LNM-specific methylation signatures were identified de novo and technically validated. A derived 3-marker methylation model for LNM diagnosis was established that achieved an AUC of 0.87 and 0.88, corresponding to the specificity of 80.9% and 85.7%, sensitivity of 80.6% and 78.1%, and accuracy of 80.8% and 83.8% in the test set of model development cohort and validation cohort, respectively. Notably, this methylation model outperformed computed tomography (CT)-based imaging with a superior AUC (0.88 vs. 0.57, p < 0.0001) and individual clinicopathological features in the validation cohort. The model integrated with clinicopathological features demonstrated further enhanced AUCs of 0.89 in the same cohort. The 3-marker methylation model and integrated model reduced 39.4% and 41.5% overtreatment as compared to standard workups, respectively.

Conclusions

A novel 3-marker methylation model was established and validated that shows diagnostic potential to identify LNM in EGC patients and thus reduce unnecessary gastrectomy in EGC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-021-01219-x.

Methylation-Driven Gene PLAU as a Potential Prognostic Marker for Differential Thyroid Carcinoma

Purpose: Aberrant DNA methylation plays a crucial role in the tumorigenesis of differentiated thyroid cancer (DTC); nevertheless, the factors leading to the local and regional recurrence of DTC are not well understood. This study aimed to establish the connection between DNA methylation-driven genes and the recurrence of DTC.

Methods: RNA sequencing profiles and DNA methylation profiles of DTC were downloaded from The Cancer Genome Atlas (TCGA) database. Combined application of the methylmix R package and univariate Cox regression analyses were used to screen and distinguish prognosis-related methylation-driven genes. Multivariate Cox regression analyses were utilized to identify the target genes that were closely associated with the recurrence of DTC. Then, correlations between the expression levels of the target genes and the clinicopathological features were verified, as well as their potential biological functions.

Results: A total of 168 Methylation-driven genes were differentially expressed in thyroid cancer, among which 10 genes (GSTO2, GSTM5, GSTM1, GPX7, FGF2, LIF, PLAU, BCL10, SHARPIN and TNFRSF1A) were identified as Hub genes. We selected PLAU for further analysis because PLAU was most strongly correlated with DTC recurrence and the DNA methylation levels of PLAU were closely associated with multiple clinicopathological features of DTC. PLAU was significantly upregulated in DTC, and patients with a high expression level of PLAU had a higher risk of recurrence (p < 0.05). Functional predictions suggested that PLAU-related genes were mainly involved in the regulation of immune-related signaling pathways. Moreover, the mRNA level of PLAU was found to be positively correlated with the cell markers of neutrophils and dendritic cells. In addition, we found that two DNA methylation sites (cg06829584, cg19399285) were associated with abnormal expression of PLAU in DTC.

Conclusion: The methylation-driven gene PLAU is an independent risk factor for the recurrence of DTC and it functions as an oncogene through the regulation of immune-related signaling pathways, which offers new insight into the molecular mechanisms of thyroid cancer and provides new possibilities for individualized treatment of thyroid cancer patients.

Identification of condition-specific biomarker systems in uterine cancer

Uterine cancer is the fourth most common cancer among women, projected to affect 66,000 US women in 2021. Uterine cancer often arises in the inner lining of the uterus, known as the endometrium, but can present as several different types of cancer, including endometrioid cancer, serous adenocarcinoma, and uterine carcinosarcoma. Previous studies have analyzed the genetic changes between normal and cancerous uterine tissue to identify specific genes of interest, including TP53 and PTEN. Here we used Gaussian Mixture Models to build condition-specific gene coexpression networks for endometrial cancer, uterine carcinosarcoma, and normal uterine tissue. We then incorporated uterine regulatory edges and investigated potential coregulation relationships. These networks were further validated using differential expression analysis, functional enrichment, and a statistical analysis comparing the expression of transcription factors and their target genes across cancerous and normal uterine samples. These networks allow for a more comprehensive look into the biological networks and pathways affected in uterine cancer compared with previous singular gene analyses. We hope this study can be incorporated into existing knowledge surrounding the genetics of uterine cancer and soon become clinical biomarkers as a tool for better prognosis and treatment.

Machine learning and deep learning methods that use omics data for metastasis prediction

Knowing metastasis is the primary cause of cancer-related deaths, incentivized research directed towards unraveling the complex cellular processes that drive the metastasis. Advancement in technology and specifically the advent of high-throughput sequencing provides knowledge of such processes. This knowledge led to the development of therapeutic and clinical applications, and is now being used to predict the onset of metastasis to improve diagnostics and disease therapies. In this regard, predicting metastasis onset has also been explored using artificial intelligence approaches that are machine learning, and more recently, deep learning-based. This review summarizes the different machine learning and deep learning-based metastasis prediction methods developed to date. We also detail the different types of molecular data used to build the models and the critical signatures derived from the different methods. We further highlight the challenges associated with using machine learning and deep learning methods, and provide suggestions to improve the predictive performance of such methods.

MetaCancer: A deep learning-based pan-cancer metastasis prediction model developed using multi-omics data

Predicting metastasis in the early stages means that clinicians have more time to adjust a treatment regimen to target the primary and metastasized cancer. In this regard, several computational approaches are being developed to identify metastasis early. However, most of the approaches focus on changes on one genomic level only, and they are not being developed from a pan-cancer perspective. Thus, we here present a deep learning (DL)-based model, MetaCancer, that differentiates pan-cancer metastasis status based on three heterogeneous data layers. In particular, we built the DL-based model using 400 patients' data that includes RNA sequencing (RNA-Seq), microRNA sequencing (microRNA-Seq), and DNA methylation data from The Cancer Genome Atlas (TCGA). We quantitatively assess the proposed convolutional variational autoencoder (CVAE) and alternative feature extraction methods. We further show that integrating mRNA, microRNA, and DNA methylation data as features improves our model's performance compared to when we used mRNA data only. In addition, we show that the mRNA-related features make a more significant contribution when attempting to distinguish the primary tumors from metastatic ones computationally. Lastly, we show that our DL model significantly outperformed a machine learning (ML) ensemble method based on various metrics.

FUT8 and Protein Core Fucosylation in Tumours: From Diagnosis to Treatment

Glycosylation changes are key molecular events in tumorigenesis, progression and glycosyltransferases play a vital role in the this process. FUT8 belongs to the fucosyltransferase family and is the key enzyme involved in N-glycan core fucosylation. FUT8 and/or core fucosylated proteins are frequently upregulated in liver, lung, colorectal, pancreas, prostate,breast, oral cavity, oesophagus, and thyroid tumours, diffuse large B-cell lymphoma, ependymoma, medulloblastoma and glioblastoma multiforme and downregulated in gastric cancer. They can be used as markers of cancer diagnosis, occurrence, progression and prognosis. Core fucosylated EGFR, TGFBR, E-cadherin, PD1/PD-L1 and α3β1 integrin are potential targets for tumour therapy. In addition, IGg1 antibody defucosylation can improve antibody affinity, which is another aspect of FUT8 that could be applied to tumour therapy.

True significance of N-acetylglucosaminyltransferases GnT-III, V and α1,6 fucosyltransferase in epithelial-mesenchymal transition and cancer

It is well known that numerous cancer-related changes occur in glycans that are attached to glycoproteins, glycolipids and proteoglycans on the cell surface and these changes in structure and the expression of the glycans are largely regulated by glycosyl-transferases, glycosidases, nucleotide sugars and their related genes. Such structural changes in glycans on cell surface proteins may accelerate the progression, invasion and metastasis of cancer cells. Among the over 200 known glycosyltransferases and related genes, β 1,6 N-acetylglucosaminyltransferase V (GnT-V) (the MGAT5 gene) and α 1,6 fucosyltransferase (FUT8) (the FUT8 gene) are representative enzymes in this respect because changes in glycans caused by these genes appear to be related to cancer metastasis and invasion in vitro as well as in vivo, and a number of reports on these genes in related to epithelial-mesenchymal transition (EMT) have also appeared. Another enzyme, one of the N-glycan branching enzymes, β1,4 N-acetylglucosaminyltransferase III (GnT-III) (the MGAT3 gene) has been reported to suppress EMT. However, there are intermediate states between EMT and mesenchymal-epithelial transition (MET) and some of these genes have been implicated in both EMT and MET and are also probably in an intermediate state. Therefore, it would be difficult to clearly define which specific glycosyltransferase is involved in EMT or MET or an intermediate state. The significance of EMT and N-glycan branching glycosyltransferases needs to be reconsidered and the inhibition of their corresponding genes would also be desirable in therapeutics. This review mainly focuses on GnT-III, GnT-V and FUT8, major players as N-glycan branching enzymes in cancer in relation to EMT programs, and also discusses the catalytic mechanisms of GnT-V and FUT8 whose crystal structures have now been obtained.

Methylation in HOX Clusters and Its Applications in Cancer Therapy

HOX genes are commonly known for their role in embryonic development, defining the positional identity of most structures along the anterior–posterior axis. In postembryonic life, HOX gene aberrant expression can affect several processes involved in tumorigenesis such as proliferation, apoptosis, migration and invasion. Epigenetic modifications are implicated in gene expression deregulation, and it is accepted that methylation events affecting HOX gene expression play crucial roles in tumorigenesis. In fact, specific methylation profiles in the HOX gene sequence or in HOX-associated histones are recognized as potential biomarkers in several cancers, helping in the prediction of disease outcomes and adding information for decisions regarding the patient’s treatment. The methylation of some HOX genes can be associated with chemotherapy resistance, and its identification may suggest the use of other treatment options. The use of epigenetic drugs affecting generalized or specific DNA methylation profiles, an approach that now deserves much attention, seems likely to be a promising weapon in cancer therapy in the near future. In this review, we summarize these topics, focusing particularly on how the regulation of epigenetic processes may be used in cancer therapy.

Downregulation of RUNX3 has a poor prognosis and promotes tumor progress in kidney cancer

BACKGROUND

Kidney cancer usually shows no symptoms until the tumor is relatively large, and current drugs fail to stop the tumor recurrence. The transcriptional factor Runt-related transcription factor 3 (RUNX3) has been reported to function as a tumor suppressor in many types of cancers.

METHODS

Kidney cancer and adjacent normal tissues were collected from 12 patients to test the expression of RUNX3 by real-time quantitative PCR, immunoblotting, and immunohistochemistry. Promoter methylation status of RUNX3 was determined using methylation analysis from 103 patient samples. Kidney cancer cell lines and xenograft mouse model were used to investigate the promoter methylation and cancer progression through inhibitor treatment and loss/gain-of-function experiments.

RESULTS

RUNX3 was significantly downregulated in kidney cancer tissues and cells, which could be elevated by higher methylation status at its promoter region. RUNX3 promoter methylation was positively correlated with poor prognosis of kidney cancer. RUNX3 loss-of-function promoted the cell proliferation, migration, and invasion of kidney cancer cells, in contrast, RUNX3 overexpression inhibited the cancer cell progression. This study provides the first instance of the effect of RUNX3 expression and its promoter methylation status on kidney cancer.

CONCLUSION

Targeting RUNX3 pathway and its promoter methylation are potential therapeutic strategies to treat kidney cancer.

Integrating tumor and nodal radiomics to predict lymph node metastasis in gastric cancer

BACKGROUND

To develop and validate a radiomics method via integrating tumor and lymph node radiomics for the preoperative prediction of lymph node (LN) status in gastric cancer (GC).

MATERIALS AND METHODS

We retrospectively collected 170 contrast-enhanced abdominal CT images from GC patients. Five times repeated random hold-out experiment was employed. Tumor and nodal radiomics features were extracted from each individual tumor and LN respectively, and then multi-step feature selection was performed. The optimal tumor and nodal features were selected using Pearson correlation analysis and sequential forward floating selection (SFFS) algorithm. After feature fusion, the SFFS algorithm was used to develop radiomics signatures. The performance of the radiomics signatures developed based on logistic regression classifier was further analyzed and compared using the area under the receiver operating characteristic curve (AUC).

RESULTS

The AUC values, reported as mean ± standard deviation, were 0.9319 ± 0.0129 and 0.8546 ± 0.0261 for the training and validation cohorts respectively. The radiomic signatures could predict LN status, especially in T2-stage, diffuse-type and moderately/well differentiated GC. After integrating clinicopathologic information, the radiomic-clinicopathologic model (training cohort, 0.9432 ± 0.0129; validation cohort, 0.8764 ± 0.0322) showed a better discrimination capability than other radiomics models and clinicopathologic model. The radiomic-clinicopathologic model also showed superior performance to the gastroenterologist' decision in all experiments, and outperformed the radiologist in some experiments.

CONCLUSION

Our proposed method presented good predictive performance and great potential for predicting LNM in GC. As a noninvasive preoperative prediction tool, it can be helpful for guiding the prognosis and treatment decision-making in GC patients.

DNA Methylation Activates TP73 Expression in Hepatocellular Carcinoma and Gastrointestinal Cancer

The complexity of TP73 expression and its functionality, as well as the role of TP73 in tumorigenesis, unlike its cousin TP53, which is an established tumor suppressor, have remained elusive to date. In this study, we isolated two stem cell lines (HepCY & HepCO) from normal young and old human liver tissues. We determined TP73 expression in HepCY and HepCO, hepatocellular cancer (HCC) cell lines (HepG2, SNU398, SNU449 and SNU475), gastrointestinal cancer (GI) cell lines (Caco2 and HCT116) and normal skin fibroblasts cell line (HS27). Immunohistochemical analyses of TP73 expression was also performed in non-cancerous and adjacent cancerous liver tissues of HCC patients. The results show that TP73 expression is exclusive to the cancer cell lines and not the adjacent normal liver tissues. Moreover, methylation-specific PCR and bisulfite sequencing studies revealed that TP73 promoter is activated only in cancer cell lines by DNA methylation. Furthermore, ChIP assay results demonstrated that a chromosomal networking protein (CTCF) and tumor protein p53 (TP53) bind to TP73 promoter and regulate TP73 expression. Our observations demonstrate that a positive correlation in tumorigenesis exists between TP73 expression and DNA methylation in promoter regions of TP73. These findings may prove significant for the development of future diagnostic and therapeutic applications.

Identification of differentially expressed genes regulated by methylation in colon cancer based on bioinformatics analysis

BACKGROUND

DNA methylation, acknowledged as a key modification in the field of epigenetics, regulates gene expression at the transcriptional level. Aberrant methylation in DNA regulatory regions could upregulate oncogenes and downregulate tumor suppressor genes without changing the sequences. However, studies of methylation in the control of gene expression are still inadequate. In the present research, we performed bioinformatics analysis to clarify the function of methylation and supply candidate methylation-related biomarkers and drivers for colon cancer.

AIM

To identify and analyze methylation-regulated differentially expressed genes (MeDEGs) in colon cancer by bioinformatics analysis.

METHODS

We downloaded RNA expression profiles, Illumina Human Methylation 450K BeadChip data, and clinical data of colon cancer from The Cancer Genome Atlas project. MeDEGs were identified by analyzing the gene expression and methylation levels using the edgeR and limma package in R software. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed in the DAVID database and KEGG Orthology-Based Annotation System 3.0, respectively. We then conducted Kaplan–Meier survival analysis to explore the relationship between methylation and expression and prognosis. Gene set enrichment analysis (GSEA) and investigation of protein-protein interactions (PPI) were performed to clarify the function of prognosis-related genes.

RESULTS

A total of 5 up-regulated and 81 down-regulated genes were identified as MeDEGs. GO and KEGG pathway analyses indicated that MeDEGs were enriched in multiple cancer-related terms. Furthermore, Kaplan–Meier survival analysis showed that the prognosis was negatively associated with the methylation status of glial cell-derived neurotrophic factor (GDNF) and reelin (RELN). In PPI networks, GDNF and RELN interact with neural cell adhesion molecule 1. Besides, GDNF can interact with GDNF family receptor alpha (GFRA1), GFRA2, GFRA3, and RET. RELN can interact with RAFAH1B1, disabled homolog 1, very low-density lipoprotein receptor, lipoprotein receptor-related protein 8, and NMDA 2B. Based on GSEA, hypermethylation of GDNF and RELN were both significantly associated with pathways including “RNA degradation,” “ribosome,” “mismatch repair,” “cell cycle” and “base excision repair.”

CONCLUSION

Aberrant DNA methylation plays an important role in colon cancer progression. MeDEGs that are associated with the overall survival of patients may be potential targets in tumor diagnosis and treatment.

MicroRNA profiling of pancreatic ductal adenocarcinoma (PDAC) reveals signature expression related to lymph node metastasis

Lymph node (LN) metastasis occurs frequently in pancreatic ductal adenocarcinoma (PDAC), representing an advanced disease stage and independently predicting patient survival. Current nodal staging is inadequate preoperatively and even less so postoperatively, and molecular biomarkers are needed to improve prognostication and selection of therapy. Recent data have suggested important roles of miRNAs in PDAC tumorigenesis and progression. The aim of the present study was to identify miRNA expression signature for nodal spread in PDAC patients. Using PDAC human tissue specimens, we identified 39 miRNAs which were differently expressed in LN positive compared to LN negative PDAC samples. Of them, six miRNAs have been reported to play a role in cancer invasion and metastasis. A high versus low six- miRNA signature score was predictive of LN metastasis in the PDAC validation cohort. We demonstrated a similar expression pattern of four out of the six miRNAs in the plasma of PDAC patients. The results of our in-vitro studies revealed that miR-141 and miR-720 are involved in the process of epithelial to mesenchymal-transition in PDAC. These miRNAs significantly inhibited in vitro proliferation, migration and invasion of PDAC cells as evidence by gain- and loss- of function studies, specifically, via ZEB-1 and TWIST1 transcription factors, as well as through the activation of the MAP4K4/JNK signaling pathway.

SMYD3 promoter hypomethylation is associated with the risk of colorectal cancer

AIM

SMYD3 encodes histone lysine methyltransferase. The goal of our study was to investigate the association between SMYD3 methylation and colorectal cancer (CRC).

MATERIALS & METHODS

SMYD3 methylation was measured by quantitative methylation-specific PCR method in 117 pairs of CRC tumor and para-tumor tissues.

RESULTS

Significantly lower SMYD3 methylation was observed in CRC tumor tissues than para-tumor tissues (p = 0.002). Further subgroup analysis by clinical features showed that significantly lower SMYD3 methylation were only observed in the CRC patients with tumors of moderately and well differentiation, positive lymph node metastasis, and stage III + IV.

CONCLUSION

Our work reported for the first time that SMYD3 promoter hypomethylation was associated with CRC.