Epigenome-Wide Tumor DNA Methylation Profiling Identifies Novel Prognostic Biomarkers of Metastatic-Lethal Progression in Men Diagnosed with Clinically Localized Prostate Cancer

ATP11A Promotes Epithelial-mesenchymal Transition in Gastric Cancer Cells via the Hippo Pathway

Background: ATP11A, a P-type ATPase, functions as flippases at the plasma membrane to maintain cellular function and vitality in several cancers. However, the role of ATP11A in gastric cancer remains unknown. This study aimed to identify ATP11A related to the biological behavior of gastric cancer, and elucidate the underlying mechanism. Methods: The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases were used to analyze the expression and prognosis of ATP11A. The biofunctions of ATP11A were explored through Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA). The expression of ATP11A were validated by immunohistochemistry (IHC), qRT-PCR and Western blotting. Transwell, wound healing, CCK8 and colony-formation were to detected the migration, invasion and proliferation of gastric cancer cells. The epithelial-mesenchymal transition (EMT) and Hippo pathway markers were examined by Western blotting. Results: The expression of ATP11A was higher in gastric cancer tissues than in normal tissues, and high ATP11A levels were related to worse prognosis of gastric cancer patients. Additionally, we proved that ATP11A promoted the migration, invasion and proliferation in gastric cancer cells. Furthermore, ATP11A was found to promote EMT by devitalizing the Hippo pathway. Conclusion: ATP11A promoted migration, invasion, proliferation and EMT via Hippo signaling devitalization in gastric cancer cells.

Role of N6‑methyladenosine in the pathogenesis, diagnosis and treatment of prostate cancer (Review)

Prostate cancer (PCa) affects males of all racial and ethnic groups, and leads to higher rates of mortality in those belonging to a lower socioeconomic status due to the late detection of the disease. PCa affects middle‑aged males between the ages of 45 and 60 years, and is the highest cause of cancer‑associated mortality in Western countries. As the most abundant and common mRNA modification in higher eukaryotes, N6‑methyladenosine (m6A) is widely distributed in mammalian cells and influences various aspects of mRNA metabolism. Recent studies have found that abnormal expression levels of various m6A regulators significantly affect the development and progression of various types of cancer, including PCa. The present review discusses the influence of m6A regulatory factors on the pathogenesis and progression of PCa through mRNA modification based on the current state of research on m6A methylation modification in PCa. It is considered that the treatment of PCa with micro‑molecular drugs that target the epigenetics of the m6A regulator to correct abnormal m6A modifications is a direction for future research into current diagnostic and therapeutic approaches for PCa.

Identification of cell-specific epigenetic patterns associated with chondroitin sulfate treatment response in an endemic arthritis, Kashin-Beck disease

Aims

To assess the alterations in cell-specific DNA methylation associated with chondroitin sulphate response using peripheral blood collected from Kashin-Beck disease (KBD) patients before initiation of chondroitin sulphate treatment.

Methods

Peripheral blood samples were collected from KBD patients at baseline of chondroitin sulphate treatment. Methylation profiles were generated using reduced representation bisulphite sequencing (RRBS) from peripheral blood. Differentially methylated regions (DMRs) were identified using MethylKit, while DMR-related genes were defined as those annotated to the gene body or 2.2-kilobase upstream regions of DMRs. Selected DMR-related genes were further validated by quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) to assess expression levels. Tensor composition analysis was performed to identify cell-specific differential DNA methylation from bulk tissue.

Results

This study revealed 21,060 hypermethylated and 44,472 hypomethylated DMRs, and 13,194 hypermethylated and 22,448 hypomethylated CpG islands for differential global methylation for chondroitin sulphate treatment response. A total of 12,666 DMR-related genes containing DMRs were identified in their promoter regions, such as CHL1 (false discovery rate (FDR) = 2.11 × 10-11), RIC8A (FDR = 7.05 × 10-4), and SOX12 (FDR = 1.43 × 10-3). Additionally, RIC8A and CHL1 were hypermethylated in responders, while SOX12 was hypomethylated in responders, all showing decreased gene expression. The patterns of cell-specific differential global methylation associated with chondroitin sulphate response were observed. Specifically, we found that DMRs located in TESPA1 and ATP11A exhibited differential DNA methylation between responders and non-responders in granulocytes, monocytes, and B cells.

Conclusion

Our study identified cell-specific changes in DNA methylation associated with chondroitin sulphate response in KBD patients.

Precision medicine for prostate cancer: An international perspective

Greater personalization of cancer medicine continues to shape therapy development and patient selection accordingly. The treatment of prostate cancer has evolved considerably since the discovery of androgen deprivation therapy. The comprehensive profiling of the prostate cancer genome has mapped the targetable molecular landscape of the disease and identified opportunities for the implementation of novel and combination therapies. In this review, we provide an overview of the molecular biology of prostate cancer and tools developed to aid prognostication and prediction of therapy benefit. Modern treatment of advanced prostate cancer is reviewed as a paradigm of increasing precision-informed approach to patient care, and must be considered on a global scale with respect to the state of science and care delivery.

Flipping the script: Advances in understanding how and why P4-ATPases flip lipid across membranes

Type IV P-type ATPases (P4-ATPases) are a family of transmembrane enzymes that translocate lipid substrates from the outer to the inner leaflet of biological membranes and thus create an asymmetrical distribution of lipids within membranes. On the cellular level, this asymmetry is essential for maintaining the integrity and functionality of biological membranes, creating platforms for signaling events and facilitating vesicular trafficking. On the organismal level, this asymmetry has been shown to be important in maintaining blood homeostasis, liver metabolism, neural development, and the immune response. Indeed, dysregulation of P4-ATPases has been linked to several diseases; including anemia, cholestasis, neurological disease, and several cancers. This review will discuss the evolutionary transition of P4-ATPases from cation pumps to lipid flippases, the new lipid substrates that have been discovered, the significant advances that have been achieved in recent years regarding the structural mechanisms underlying the recognition and flipping of specific lipids across biological membranes, and the consequences of P4-ATPase dysfunction on cellular and physiological functions. Additionally, we emphasize the requirement for additional research to comprehensively understand the involvement of flippases in cellular physiology and disease and to explore their potential as targets for therapeutics in treating a variety of illnesses. The discussion in this review will primarily focus on the budding yeast, C. elegans, and mammalian P4-ATPases.

miR-181-5p/KLHL5 Promoted Proliferation and Migration of Gastric Cancer Through Activating METTL3-Mediated m6A Process

KLHL5 was a member of kelch-repeat protein family and was involved in the initiation of progression of a plethora of cancers. However, its specific role in gastric cancer was not explicitly illustrated. In this context, we aimed to investigate the biological role and mechanisms about KLHL5 in gastric cancer. qRT-PCR and western blot were used to investigate the expression of KLHL5 and EMT biomarkers. Wound healing assay, CCK-8, and Transwell assay were used to investigate the biological function of KLHL5. We found that KLHL5 was highly expressed in gastric cancer both in vivo and in vitro; besides, its high expression led to a shorter overall survival. Following statistical analysis showed that KLHL5 was associated with M stage. As for molecular experiments, we found that KLHL5 knockdown significantly reduced the proliferation, migration, and invasion ability of gastric cancer cell line MKN45 and SGC-7901. Furthermore, we found that miR-181-5p targeted KLHL5 to regulate m6A level through METTL3. In addition, KLHL5 knockdown could significantly reduce the lung metastasis rate in mice. In conclusion, we found that miR-181-5p/KLHL5 could promote the proliferation, migration, and invasion of gastric cancer by activating m6A process through regulating METTL3.

Genome-wide methylation profiling of diagnostic tumor specimens identified DNA methylation markers associated with metastasis among men with untreated localized prostate cancer

BACKGROUND

We used a genome-wide discovery approach to identify methylation markers associated with metastasis in men with localized prostate cancer (PCa), as better identification of those at high risk of metastasis can inform treatment decision-making.

METHODS

We identified men with localized PCa at Kaiser Permanente California (January 1, 1997-December 31, 2006) who did not receive curative treatment and followed them for 10 years to determine metastasis status. Cases were chart review-confirmed metastasis, and controls were matched using density sampling. We extracted DNA from the cancerous areas in the archived diagnostic tissue blocks. We used Illumina's Infinium MethylationEPIC BeadChip for methylation interrogation. We used conditional logistic regression and Bonferroni's correction to identify methylation markers associated with metastasis. In a separate validation cohort (2007), we evaluated the added predictive utility of the methylation score beyond clinical risk score.

RESULTS

Among 215 cases and 404 controls, 31 CpG sites were significantly associated with metastasis status. Adding the methylation score to the clinical risk score did not meaningfully improve the c-statistic (0.80-0.81) in the validation cohort, though the score itself was statistically significant (p < 0.01). In the validation cohort, both clinical risk score alone and methylation marker score alone are well calibrated for predicted 10-year metastasis risks. Adding the methylation score to the clinical risk score only marginally improved predictive risk calibration.

CONCLUSION

Our findings do not support the use of these markers to improve clinical risk prediction. The methylation markers identified may inform novel hypothesis in the roles of these genetic regions in metastasis development.

Type IV P-Type ATPases: Recent Updates in Cancer Development, Progression, and Treatment

Adaptations of cancer cells for survival are remarkable. One of the most significant properties of cancer cells to prevent the immune system response and resist chemotherapy is the altered lipid metabolism and resulting irregular cell membrane composition. The phospholipid distribution in the plasma membrane of normal animal cells is distinctly asymmetric. Lipid flippases are a family of enzymes regulating membrane asymmetry, and the main class of flippases are type IV P-type ATPases (P4-ATPases). Alteration in the function of flippases results in changes to membrane organization. For some lipids, such as phosphatidylserine, the changes are so drastic that they are considered cancer biomarkers. This review will analyze and discuss recent publications highlighting the role that P4-ATPases play in the development and progression of various cancer types, as well as prospects of targeting P4-ATPases for anti-cancer treatment.

Molecular profiling of osteoprogenitor cells reveals FOS as a master regulator of bone non-union

Despite the advances in bone fracture treatment, a significant fraction of fracture patients will develop non-union. Most non-unions are treated with surgery since identifying the molecular causes of these defects is exceptionally challenging. In this study, compared with marrow bone, we generated a transcriptional atlas of human osteoprogenitor cells derived from healing callus and non-union fractures. Detailed comparison among the three conditions revealed a substantial similarity of callus and nonunion at the gene expression level. Nevertheless, when assayed functionally, they showed different osteogenic potential. Utilizing longitudinal transcriptional profiling of the osteoprogenitor cells, we identified FOS as a putative master regulator of non-union fractures. We validated FOS activity by profiling a validation cohort of 31 tissue samples. Our work identified new molecular targets for non-union classification and treatment while providing a valuable resource to better understand human bone healing biology.

Functional impact of multi-omic interactions in breast cancer subtypes

Multi-omic approaches are expected to deliver a broader molecular view of cancer. However, the promised mechanistic explanations have not quite settled yet. Here, we propose a theoretical and computational analysis framework to semi-automatically produce network models of the regulatory constraints influencing a biological function. This way, we identified functions significantly enriched on the analyzed omics and described associated features, for each of the four breast cancer molecular subtypes. For instance, we identified functions sustaining over-representation of invasion-related processes in the basal subtype and DNA modification processes in the normal tissue. We found limited overlap on the omics-associated functions between subtypes; however, a startling feature intersection within subtype functions also emerged. The examples presented highlight new, potentially regulatory features, with sound biological reasons to expect a connection with the functions. Multi-omic regulatory networks thus constitute reliable models of the way omics are connected, demonstrating a capability for systematic generation of mechanistic hypothesis.

Biomarkers for the Detection and Risk Stratification of Aggressive Prostate Cancer

Current strategies for the clinical management of prostate cancer are inadequate for a precise risk stratification between indolent and aggressive tumors. Recently developed tissue-based molecular biomarkers have refined the risk assessment of the disease. The characterization of tissue biopsy components and subsequent identification of relevant tissue-based molecular alterations have the potential to improve the clinical decision making and patient outcomes. However, tissue biopsies are invasive and spatially restricted due to tumor heterogeneity. Therefore, there is an urgent need for complementary diagnostic and prognostic options. Liquid biopsy approaches are minimally invasive with potential utility for the early detection, risk stratification, and monitoring of tumors. In this review, we focus on tissue and liquid biopsy biomarkers for early diagnosis and risk stratification of prostate cancer, including modifications on the genomic, epigenomic, transcriptomic, and proteomic levels. High-risk molecular alterations combined with orthogonal clinical parameters can improve the identification of aggressive tumors and increase patient survival.

Temporal proteomic analyses of human lung cells distinguish high pathogenicity influenza viruses and coronaviruses from low pathogenicity viruses

Newly re-emerging viruses are of significant global concern. In late 2019, a new coronavirus, SARS-CoV-2, emerged in China and soon spread worldwide, causing the COVID-19 pandemic, which to date has caused >6 M deaths. There has been a wealth of studies on this new virus since its emergence. The coronaviruses consist of many animal and human pathogens, with some of the human coronavirus, such as strain OC43, normally causing only mild cold-like symptoms. Viruses usurp host cellular processes to successfully replicate. We used tandem mass tag mass spectrometry-based proteomic analyses of human lung MRC-5 cells infected with OC43 for various periods of time to delineate virus-induced host cell alterations. Numerous proteins involved in lipid metabolism, molecular transport, small molecule biochemistry, cell death and survival, humoral immune response, and inflammatory response were dysregulated. Comparison of our findings to previous studies that examined a range of differentially pathogenic influenza A viruses (IAV), and to SARS-CoV-2 data, revealed that proteins involved in the cell cycle, cytokine signaling, DNA replication, and anti-inflammatory responses were generally similarly affected by virtually all tested IAV and CoV. However, proteins involved in necrosis, protein metabolism, ECM regulation, and signal transduction were generally different. In addition, the more pathogenic CoV and IAV activated Rb-dependent repression of E2F-mediated transcription, whereas less pathogenic influenza and coronaviruses either inhibited or had no effect on this pathway.

Accurate detection of benign and malignant renal tumor subtypes with MethylBoostER: An epigenetic marker-driven learning framework

Current gold standard diagnostic strategies are unable to accurately differentiate malignant from benign small renal masses preoperatively; consequently, 20% of patients undergo unnecessary surgery. Devising a more confident presurgical diagnosis is key to improving treatment decision-making. We therefore developed MethylBoostER, a machine learning model leveraging DNA methylation data from 1228 tissue samples, to classify pathological subtypes of renal tumors (benign oncocytoma, clear cell, papillary, and chromophobe RCC) and normal kidney. The prediction accuracy in the testing set was 0.960, with class-wise ROC AUCs >0.988 for all classes. External validation was performed on >500 samples from four independent datasets, achieving AUCs >0.89 for all classes and average accuracies of 0.824, 0.703, 0.875, and 0.894 for the four datasets. Furthermore, consistent classification of multiregion samples (N = 185) from the same patient demonstrates that methylation heterogeneity does not limit model applicability. Following further clinical studies, MethylBoostER could facilitate a more confident presurgical diagnosis to guide treatment decision-making in the future.

Methylation Subtypes of Primary Prostate Cancer Predict Poor Prognosis

BACKGROUND

Patients with prostate cancer experience heterogeneous outcomes after radical prostatectomy. Genomic studies including The Cancer Genome Atlas (TCGA) have reported molecular signatures of prostate cancer, but few studies have assessed the prognostic effects of DNA methylation profiles.

METHODS

We conducted the largest methylome subtyping analysis for primary prostate tumors to date, using methylome data from three patient populations: TCGA, a prostate cancer cohort study conducted at the Fred Hutchinson Cancer Research Center (FH; Seattle, WA), and the Canadian International Cancer Genome Consortium (ICGC) cohort. Four subtypes were detected in the TCGA dataset, then independently assigned to FH and ICGC cohort data. The identified methylation subtypes were assessed for association with cancer prognosis in the above three patient populations.

RESULTS

Using a set of hypermethylated CpG sites, four methylation subtypes were identified in TCGA. Compared with subtype 1, subtype 4 had an HR of 2.09 (P = 0.029) for biochemical recurrence (BCR) in TCGA patients. HRs of 2.76 (P = 0.002) for recurrence and 9.73 (P = 0.002) for metastatic-lethal (metastasis or prostate cancer-specific death) outcomes were observed in the FH cohort. A similar pattern of association was noted in the Canadian ICGC cohort, though HRs were not statistically significant.

CONCLUSIONS

A hypermethylated subtype was associated with an increased hazard of recurrence and mortality in three studies with prostate tumor methylome data. Further molecular work is needed to understand the effect of methylation subtypes on cancer prognosis.

IMPACT

This study identified a DNA methylation subtype that was associated with worse prostate cancer prognosis after radical prostatectomy.

Deciphering the Increased Prevalence of TP53 Mutations in Metastatic Prostate Cancer

The prevalence of TP53 mutations in advanced prostate cancers (PCa) is 3 to 5 times of the quantity in primary PCa. By an integrative analysis of the Cancer Genome Atlas and Catalogue of Somatic Mutations in Cancer data, we revealed the supporting evidence for 2 complementary hypotheses: H₁ - TP53 abnormalities promote metastasis or therapy-resistance of PCa cells, and H₂—part of TP53 mutations in PCa metastases occur after the diagnosis of original cancers. The plausibility of these hypotheses can explain the increased prevalence of TP53 mutations in PCa metastases. With H₁ and H₂ as the general assumptions, we developed mathematical models to decipher the change of the percentage frequency (prevalence) of TP53 mutations from primary tumors to metastases. The following results were obtained. Compared to TP53-normal patients, TP53-mutated patients had poorer biochemical relapse-free survival, higher Gleason scores, and more advanced t-stages (P < .01). Single-nucleotide variants in metastases more frequently occurred on G bases of the coding sequence than those in primary cancers (P = .03). The profile of TP53 hotspot mutations was significantly different between primary and metastatic PCa as demonstrated in a set of statistical tests (P < .05). By the derived formulae, we estimated that about 40% TP53 mutation records collected from metastases occurred after the diagnosis of the original cancers. Our study provided significant insight into PCa progression. The proposed models can also be applied to decipher the prevalence of mutations on TP53 (or other driver genes) in other cancer types.

ATP11A promotes EMT by regulating Numb PRRL in pancreatic cancer cells

Purpose

The Numb protein plays a vital role in tumor development. The main aim of this study was to identify ATP11A, which is associated with the biological behavior of pancreatic cancer, and elucidate its relationship with Numb and the underlying mechanism behind this relationship.

Methods

First, data retrieved from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEX) databases was used to investigate the expression of ATP11A mRNA and its relationship with Numb mRNA in pancreatic cancer. Western blot assays on 31 pairs of pancreatic cancer tissues and paracancerous tissues, and immunohistochemical assays on 81 pancreatic cancer specimens were performed in order to verify the expression of ATP11A in pancreatic cancer at the protein level. Next, ATP11A was overexpressed or knocked down to observe its effects on the invasion and migration ability of pancreatic cancer cells and the changes of downstream proteins. Rescue assays were conducted to determine the mechanism through which ATP11A affects Numb, ZEB1, Snail2 and other proteins. Furthermore, immunoprecipitation assays were performed to explore the interaction between ATP11A and Numb. Finally, pancreatic cancer cells were stimulated with TGFB1 and ATP11A expression was examined to explore whether the effect of ATP11A on EMT was TGFB dependent.

Results

At the mRNA level, the expression of ATP11A in pancreatic cancer tissues was significantly higher than in normal pancreatic tissues (P < 0.001). ATP11A expression was also highly correlated with Numb expression (R = 0.676). At the protein level, ATP11A expression in pancreatic cancer tissues was significantly higher than that in paracancerous tissues (P = 0.0009), and high ATP11A expression was also correlated with a worse prognosis. Moreover, our results showed that ATP11A can promote the invasion and migration of pancreatic cancer cells. Additionally, ATP11A could positively regulate the expression of Numb PRRL, Snail2 and ZEB1 proteins. The rescue experiment results showed that the enhancement effect of ATP11A on ZEB1/Snail2 was suppressed by the specific knockdown of Numb PRRL. In addition, the immunoprecipitation results showed that ATP11A could specifically bind to Numb PRRL. The expression of ATP11A was also upregulated after TGFB stimulation, suggesting that the effect of ATP11A on EMT is TGFB dependent.

Conclusion

ATP11A is significantly upregulated in pancreatic cancer tissues, where it promotes the invasion and migration ability of pancreatic cancer cells. It is also associated with adverse prognosis in pancreatic cancer. Furthermore, ATP11A affects the epithelial-to-mesenchymal transition (EMT) of pancreatic cancer by regulating the TGFB dependent Numb PRRL-ZEB1/Snail2 pathway.

Integrated Analysis of miR-7-5p-Related ceRNA Network Reveals Potential Biomarkers for the Clinical Outcome of Gastric Cancer

Gastric cancer (GC) is the second leading cause of tumor-associated death and the fourth most commonly seen tumor across the world. Abnormal ncRNAs have been verified to be involved in potential metastasis via modulating epithelial-to-mesenchymal transition progression and are vital for the progression of cancers. Tumor-infiltrating immune cells (TICs) are a vital indicator of whether cancer patients will benefit from immunotherapy. Nonetheless, the association between ceRNAs and immune cells remained largely unclear. We used the ceRNA network combined with TICs for the prediction of the clinical outcome of GC patients based on TCGA datasets. The percentage of immunocytes in GC was speculated by the use of CIBERSORT. Via Lasso and multivariate assays, prognostic models were established applying survival-related genes and immune cells. Nomograms were developed, and the accuracy of the nomograms was determined using calibration curves. The association between ceRNAs and TICs was validated by the use of integration analysis. In this study, there were 2219 mRNAs (1308 increased and 911 decreased), 171 lncRNAs (51 decreased and 120 increased), and 123 miRNAs (55 decreased and 68 increased) differentially expressed between tumor groups and nontumor groups. Five lncRNAs, six miRNAs, and 64 mRNAs were used for ceRNA network construction. Eight genes including LOX, SPARC, MASTL, PI15, BMPR1B, ANKRD13B, PVT1, and miR-7-5p were applied for the development of the prognostic model. Survival assays suggested that tumor cases with high risk exhibited a shorter overall survival. In addition, we included T-cell CD4 memory activated, monocytes, and neutrophils for the development of a prognosis model. Eventually, our team demonstrated the possible associations between the ceRNA prognosis model and prognostic model based on immune cells. To sum up, the ceRNA network could be used for gene regulation and predict clinical outcomes of GC patients.

Recent Multiomics Approaches in Endometrial Cancer

Endometrial cancer is the most common gynecological cancers in developed countries. Many of the mechanisms involved in its initiation and progression remain unclear. Analysis providing comprehensive data on the genome, transcriptome, proteome, and epigenome could help in selecting molecular markers and targets in endometrial cancer. Multiomics approaches can reveal disturbances in multiple biological systems, giving a broader picture of the problem. However, they provide a large amount of data that require processing and further integration prior to analysis. There are several repositories of multiomics datasets, including endometrial cancer data, as well as portals allowing multiomics data analysis and visualization, including Oncomine, UALCAN, LinkedOmics, and miRDB. Multiomics approaches have also been applied in endometrial cancer research in order to identify novel molecular markers and therapeutic targets. This review describes in detail the latest findings on multiomics approaches in endometrial cancer.

Suppression of m6A mRNA modification by DNA hypermethylated ALKBH5 aggravates the oncological behavior of KRAS mutation/LKB1 loss lung cancer

Oncogenic KRAS mutations combined with the loss of the LKB1 tumor-suppressor gene (KL) are strongly associated with aggressive forms of lung cancer. N6-methyladenosine (m6A) in mRNA is a crucial epigenetic modification that controls cancer self-renewal and progression. However, the regulation and role of m6A modification in this cancer are unclear. We found that decreased m6A levels correlated with the disease progression and poor survival for KL patients. The correlation was mediated by a special increase in ALKBH5 (AlkB family member 5) levels, an m6A demethylase. ALKBH5 gain- or loss-of function could effectively reverse LKB1 regulated cell proliferation, colony formation, and migration of KRAS-mutated lung cancer cells. Mechanistically, LKB1 loss upregulated ALKBH5 expression by DNA hypermethylation of the CTCF-binding motif on the ALKBH5 promoter, which inhibited CTCF binding but enhanced histone modifications, including H3K4me3, H3K9ac, and H3K27ac. This effect could successfully be rescued by LKB1 expression. ALKBH5 demethylation of m6A stabilized oncogenic drivers, such as SOX2, SMAD7, and MYC, through a pathway dependent on YTHDF2, an m6A reader protein. The above findings were confirmed in clinical KRAS-mutated lung cancer patients. We conclude that loss of LKB1 promotes ALKBH5 transcription by a DNA methylation mechanism, reduces m6A modification, and increases the stability of m6A target oncogenes, thus contributing to aggressive phenotypes of KRAS-mutated lung cancer.

Using lncRNA Sequencing to Reveal a Putative lncRNA-mRNA Correlation Network and the Potential Role of PCBP1-AS1 in the Pathogenesis of Cervical Cancer

Background/Aims

Long non-coding RNAs (lncRNAs) play important roles in many diseases and participate in posttranscriptional regulatory networks in tumors. However, the functions of major lncRNAs in cervical cancer are unclear. Therefore, the aim of this study was to construct a lncRNA-mRNA coexpression functional network and analyze lncRNAs that might contribute to the pathogenesis of cervical cancer.

Methods

Differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) between three pairs of cervical cancer tissues and adjacent mucosa were identified by lncRNA microarray analysis. LncRNA-mRNA correlation analysis and functional enrichment were performed on the DEGs. From the correlation network, PCBP1-AS1 was selected as a candidate for further analysis. PCBP1-AS1 expression was examined by qPCR, and Kaplan-Meier survival, clinicopathology, GSEA, and immune infiltration analysis of PCBP1-AS1 were performed. The immune responses of PCBP1-AS1 expression in cervical cancer were analyzed using TIMER and western blot. PCBP1-AS1 was knocked down and overexpressed to evaluate its role in cell proliferation, migration, and invasion.

Results

A total of 130 lncRNAs were significantly differentially expressed in cervical cancer patient samples compared with control samples. Differentially expressed mRNAs in the lncRNA-mRNA interaction network were involved in the EMT process. Combined with the Kaplan-Meier survival analyses, the coexpression network revealed that PCBP1-AS1 was significantly associated with OS and clinicopathological parameters in cervical cancer patients. Moreover, PCBP1-AS1 expression was not only significantly increased in cervical cancer specimens but also associated with tumor stage, TNM, and invasion. GSEA revealed that PCBP1-AS1 is closely correlated with cell biological function via the p53 and notch signaling pathways. TIMER analysis revealed that the numbers of NK cells and M2 macrophages decreased when PCBP1-AS1 expression was high, which was consistent with the western blot results in clinical samples. Furthermore, in vitro experiments showed that high expression of PCBP1-AS1 promoted cell proliferation, migration, and invasion.

Conclusions

Transcriptomic and lncRNA-mRNA correlation analyses revealed that PCBP1-AS1 plays a key role as an independent prognostic factor in patients with cervical cancer. The identification of PCBP1-AS1 as a new biomarker for cervical cancer could help explain how changes in the immune environment promote cervical cancer development.

Genome-wide DNA methylation profiling of leukocytes identifies CpG methylation signatures of aggressive prostate cancer

Most of screening-detected prostate cancer (PCa) are indolent and not lethal. Biomarkers that can predict aggressive diseases independently of clinical features are needed to improve risk stratification of localized PCa patients and reduce overtreatment. We aimed to identify leukocyte DNA methylation differences between clinically defined aggressive and non-aggressive PCa. We performed whole genome DNA methylation profiling in leukocyte DNA from 287 PCa patients with Gleason Score (GS) 6 and ≥8 using Illumina 450k methylation arrays. We observed a global hypomethylation in GS≥8 patients compared to GS=6 PCa patients; in contrast, the methylation level in core promoter and exon 1 region was significantly higher in GS≥8 patients than GS=6 PCa. We then performed 5-fold cross validated random forest model training on 1,459 differentially methylated CpG Probes (DMPs) with false discovery rate (FDR) <0.01 between GS=6 and GS≥8 groups. The power of the predictive model was further reinforced by ranking the DMPs with Decreased Gini and re-train the model with the top 97 DMPs (Testing AUC=0.920, predict accuracy =0.847). In conclusion, we identified a CpG methylation signature in leukocyte DNA that is associated with aggressive clinical features of PCa at diagnosis.

Comprehensive Analysis of Expression Regulation for RNA m6A Regulators With Clinical Significance in Human Cancers

Background

N6-methyladenosine (m6A), the most abundant chemical modification on eukaryotic messenger RNA (mRNA), is modulated by three class of regulators namely "writers," "erasers," and "readers." Increasing studies have shown that aberrant expression of m6A regulators plays broad roles in tumorigenesis and progression. However, it is largely unknown regarding the expression regulation for RNA m6A regulators in human cancers.

Results

Here we characterized the expression profiles of RNA m6A regulators in 13 cancer types with The Cancer Genome Atlas (TCGA) data. We showed that METTL14, FTO, and ALKBH5 were down-regulated in most cancers, whereas YTHDF1 and IGF2BP3 were up-regulated in 12 cancer types except for thyroid carcinoma (THCA). Survival analysis further revealed that low expression of several m6A regulators displayed longer overall survival times. Then, we analyzed microRNA (miRNA)-regulated and DNA methylation-regulated expression changes of m6A regulators in pan-cancer. In total, we identified 158 miRNAs and 58 DNA methylation probes (DMPs) involved in expression regulation for RNA m6A regulators. Furthermore, we assessed the survival significance of those regulatory pairs. Among them, 10 miRNAs and 7 DMPs may promote cancer initiation and progression; conversely, 3 miRNA/mRNA pairs in kidney renal clear cell carcinoma (KIRC) may exert tumor-suppressor function. These findings are indicative of their potential prognostic values. Finally, we validated two of those miRNA/mRNA pairs (hsa-miR-1307-3p/METTL14 and hsa-miR-204-5p/IGF2BP3) that could serve a critical role for potential clinical application in KIRC patients.

Conclusions

Our findings highlighted the importance of upstream regulation (miRNA and DNA methylation) governing m6A regulators' expression in pan-cancer. As a result, we identified several informative regulatory pairs for prognostic stratification. Thus, our study provides new insights into molecular mechanisms of m6A modification in human cancers.

An overview of cancer health disparities: new approaches and insights and why they matter

Cancer health disparities remain stubbornly entrenched in the US health care system. The Affordable Care Act was legislation to target these disparities in health outcomes. Expanded access to health care, reduction in tobacco use, uptake of other preventive measures and cancer screening, and improved cancer therapies greatly reduced cancer mortality among women and men and underserved communities in this country. Yet, disparities in cancer outcomes remain. Underserved populations continue to experience an excessive cancer burden. This burden is largely explained by health care disparities, lifestyle factors, cultural barriers, and disparate exposures to carcinogens and pathogens, as exemplified by the COVID-19 epidemic. However, research also shows that comorbidities, social stress, ancestral and immunobiological factors, and the microbiome, may contribute to health disparities in cancer risk and survival. Recent studies revealed that comorbid conditions can induce an adverse tumor biology, leading to a more aggressive disease and decreased patient survival. In this review, we will discuss unanswered questions and new opportunities in cancer health disparity research related to comorbid chronic diseases, stress signaling, the immune response, and the microbiome, and what contribution these factors may have as causes of cancer health disparities.

Effective Prediction of Prostate Cancer Recurrence through the IQGAP1 Network

IQGAP1 expression was analyzed in: (1) primary prostate cancer, (2) xenografts produced from LNCaP, DU145, and PC3 cells, 3) tumor of PTEN-/- and TRAMP mice, and (3) castration resistant PC (CRPC) produced by LNCaP xenografts and PTEN-/- mice. IQGAP1 downregulations occurred in CRPC and advanced PCs. The downregulations were associated with rapid PC recurrence in the TCGA PanCancer (n = 492, p = 0.01) and MSKCC (n = 140, p = 4 × 10-6) cohorts. Differentially expressed genes (n = 598) relative to IQGAP1 downregulation were identified with enrichment in chemotaxis, cytokine signaling, and others along with reductions in immune responses. A novel 27-gene signature (Sig27gene) was constructed from these DEGs through random division of the TCGA cohort into a Training and Testing population. The panel was validated using an independent MSKCC cohort. Sig27gene robustly predicts PC recurrence at (hazard ratio) HR 2.72 and p < 2 × 10-16 in two independent PC cohorts. The prediction remains significant after adjusting for multiple clinical features. The novel and robust nature of Sig27gene underlie its great translational potential as a prognostic biomarker to predict PC relapse risk in patients with primary PC.

Advances in Prognostic Methylation Biomarkers for Prostate Cancer

There is a major clinical need for accurate biomarkers for prostate cancer prognosis, to better inform treatment strategies and disease monitoring. Current clinically recognised prognostic factors, including prostate-specific antigen (PSA) levels, lack sensitivity and specificity in distinguishing aggressive from indolent disease, particularly in patients with localised intermediate grade prostate cancer. There has therefore been a major focus on identifying molecular biomarkers that can add prognostic value to existing markers, including investigation of DNA methylation, which has a known role in tumorigenesis. In this review, we will provide a comprehensive overview of the current state of DNA methylation biomarker studies in prostate cancer prognosis, and highlight the advances that have been made in this field. We cover the numerous studies into well-established candidate genes, and explore the technological transition that has enabled hypothesis-free genome-wide studies and the subsequent discovery of novel prognostic genes.

Multifaceted Roles of DNA Methylation in Neoplastic Transformation, from Tumor Suppressors to EMT and Metastasis

Among the major mechanisms involved in tumorigenesis, DNA methylation is an important epigenetic modification impacting both genomic stability and gene expression. Methylation of promoter-proximal CpG islands (CGIs) and transcriptional silencing of tumor suppressors represent the best characterized epigenetic changes in neoplastic cells. The global cancer-associated effects of DNA hypomethylation influence chromatin architecture and reactivation of repetitive elements. Moreover, recent analyses of cancer cell methylomes highlight the role of the DNA hypomethylation of super-enhancer regions critically controlling the expression of key oncogenic players. We will first summarize some basic aspects of DNA methylation in tumorigenesis, along with the role of dysregulated DNA methyltransferases and TET (Ten-Eleven Translocation)-family methylcytosine dioxygenases. We will then examine the potential contribution of epimutations to causality and heritability of cancer. By reviewing some representative genes subjected to hypermethylation-mediated silencing, we will survey their oncosuppressor functions and roles as biomarkers in various types of cancer. Epithelial-to-mesenchymal transition (EMT) and the gain of stem-like properties are critically involved in cancer cell dissemination, metastasis, and therapeutic resistance. However, the driver vs passenger roles of epigenetic changes, such as DNA methylation in EMT, are still poorly understood. Therefore, we will focus our attention on several aspects of DNA methylation in control of EMT and metastasis suppressors, including both protein-coding and noncoding genes.

Identification of Pan-Cancer Prognostic Biomarkers Through Integration of Multi-Omics Data

Prognostic biomarkers dedicating to treat cancer are very difficult to identify. Although high-throughput sequencing technology allows us to mine prognostic biomarkers much deeper by analyzing omics data, there is lack of effective methods to comprehensively utilize multi-omics data. In this work, we integrated multi-omics data [DNA methylation (DM), gene expression (GE), somatic copy number alternation, and microRNA expression (ME)] and proposed a method to rank genes by desiring a “Score.” Applying the method, cancer-specific prognostic biomarkers for 13 cancers were obtained. The prognostic powers of the biomarkers were further assessed by C-indexes (ranged from 0.76 to 0.96). Moreover, by comparing the 13 survival-related gene lists, seven genes (SLK, API5, BTBD2, PTAR1, VPS37A, EIF2B1, and ZRANB1) were found to be associated with prognosis in a variety of cancers. In particular, SLK was more likely to be cancer-related due to its high missense mutation rate and associated with cell adhesion. Furthermore, after network analysis, EPRS, HNRNPA2B1, BPTF, LRRK1, and PUM1 were demonstrated to have a broad correlation with cancers. In summary, our method has a better integration of multi-omics data that can be extended to the researches of other diseases. And the prognostic biomarkers had a better prognostic power than previous methods. Our results could provide a reference for translational medicine researchers and clinicians.

DNA methylation and cis-regulation of gene expression by prostate cancer risk SNPs

Genome-wide association studies have identified more than 100 SNPs that increase the risk of prostate cancer (PrCa). We identify and compare expression quantitative trait loci (eQTLs) and CpG methylation quantitative trait loci (meQTLs) among 147 established PrCa risk SNPs in primary prostate tumors (n = 355 from a Seattle-based study and n = 495 from The Cancer Genome Atlas, TCGA) and tumor-adjacent, histologically benign samples (n = 471 from a Mayo Clinic study). The role of DNA methylation in eQTL regulation of gene expression was investigated by data triangulation using several causal inference approaches, including a proposed adaptation of the Causal Inference Test (CIT) for causal direction. Comparing eQTLs between tumors and benign samples, we show that 98 of the 147 risk SNPs were identified as eQTLs in the tumor-adjacent benign samples, and almost all 34 eQTL identified in tumor sets were also eQTLs in the benign samples. Three lines of results support the causal role of DNA methylation. First, nearly 100 of the 147 risk SNPs were identified as meQTLs in one tumor set, and almost all eQTLs in tumors were meQTLs. Second, the loss of eQTLs in tumors relative to benign samples was associated with altered DNA methylation. Third, among risk SNPs identified as both eQTLs and meQTLs, mediation analyses suggest that over two-thirds have evidence of a causal role for DNA methylation, mostly mediating genetic influence on gene expression. In summary, we provide a comprehensive catalog of eQTLs, meQTLs and putative cancer genes for known PrCa risk SNPs. We observe that a substantial portion of germline eQTL regulatory mechanisms are maintained in the tumor development, despite somatic alterations in tumor genome. Finally, our mediation analyses illuminate the likely intermediary role of CpG methylation in eQTL regulation of gene expression.

Copy number alterations are associated with metastatic-lethal progression in prostate cancer

BACKGROUNDS

Aside from Gleason score few factors accurately identify the subset of prostate cancer (PCa) patients at high risk for metastatic progression. We hypothesized that copy number alterations (CNAs), assessed using CpG methylation probes on Illumina Infinium® Human Methylation450 (HM450K) BeadChip arrays, could identify primary prostate tumors with potential to develop metastatic progression.

METHODS

Epigenome-wide DNA methylation profiling was performed in surgically resected primary tumor tissues from two cohorts of PCa patients with clinically localized disease who underwent radical prostatectomy (RP) as primary therapy and were followed prospectively for at least 5 years: (1) a Fred Hutchinson (FH) Cancer Research Center-based cohort (n = 323 patients); and (2) an Eastern Virginia (EV) Medical School-based cohort (n = 78 patients). CNAs were identified using the R package ChAMP. Metastasis was confirmed by positive bone scan, MRI, CT or biopsy, and death certificates confirmed cause of death.

RESULTS

We detected 15 recurrent CNAs were associated with metastasis in the FH cohort and replicated in the EV cohort (p < 0.05) without adjusting for Gleason score in the model. Eleven of the recurrent CNAs were associated with metastatic progression in the FH cohort and validated in the EV cohort (p < 0.05) when adjusting for Gleason score.

CONCLUSIONS

This study shows that CNAs can be reliably detected from HM450K-based DNA methylation data. There are 11 recurrent CNAs showing association with metastatic-lethal events following RP and improving prediction over Gleason score. Genes affected by these CNAs may functionally relate to tumor aggressiveness and metastatic progression.

Integration of multi-omics data to mine cancer-related gene modules

The identification of cancer-related genes is a major research goal, with implications for determining the pathogenesis of cancer and identifying biomarkers for early diagnosis and treatment. In this study, by integrating multi-omics data, including gene expression, DNA copy number variation, DNA methylation, transcription factors, miRNA, and lncRNA data, we propose a method for mining cancer-related genes based on network models. First, using random forest-based feature selection method multi-omics data are integrated to identify key regulatory factors that affect gene expression, and then genome-wide regulatory networks are constructed. Next, by comparing the regulatory networks of key candidate genes in variant samples and non-variant samples, a differential expression regulatory network is generated. The differential network contains a collection of abnormal regulatory genes of key candidate genes. Then, by introducing the functional similarity as a distance metric for gene sets, a density-based clustering method is used to mine gene modules related to cancer. We applied this method to LUSC (lung squamous cell carcinoma) and mined cancer-related gene modules composed of 20 genes. GO function and KEGG pathway analyses indicated that the modules were closely related to cancer. A survival analysis was used to verify that the excavated gene modules can effectively distinguish between high- and low-risk groups. Overall, these results suggest that the proposed method can be used to identify cancer-related gene modules, providing a basis for the development of biomarkers for diagnosis and treatment.

DNA methylation-based prognostic biomarkers of acute myeloid leukemia patients

Background

Acute myeloid leukemia (AML) is a heterogeneous clonal disease that prevents normal myeloid differentiation with its common features. Its incidence increases with age and has a poor prognosis. Studies have shown that DNA methylation and abnormal gene expression are closely related to AML.

Methods

The methylation array data and mRNA array data are from the Gene Expression Omnibus (GEO) database. Through the GEO data, we identified differential genes from tumors and normal samples. Then we performed Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses on these differential genes. Protein-protein interaction (PPI) network construction and module analysis were performed to screen the highest-scoring modules. Next, we used SurvExpress software to analyze the genes in the highest-scoring module and selected potential prognostic genes by univariate and multivariate Cox analysis. Finally, the three genes screened by SurvExpress software were analyzed using the methylation analysis site MethSurv to explore AML associated methylation biomarkers.

Results

We found three genes that can be used as independent prognostic factors for AML. These three genes are the low expression/methylation genes ATP11A and ITGAM, and the high expression/low methylation gene ZNRF2.

Conclusions

In this study, we performed a comprehensive analysis of DNA methylation and gene expression to identify key epigenetic genes in AML.

MTSS1 hypermethylation is associated with prostate cancer progression

This study was conducted to evaluate the influence of DNA methylation of metastasis suppressor 1 (MTSS1) on prostate cancer (PCa) progression. Forty-nine paired PCa tissue samples and normal tissue samples from The Cancer Genome Atlas were analyzed. Methylome analysis, CpG island arrays and Hierarchical clustering were used to analyze methylation profiles of PCa tissues. MTSS1 methylation level was detected by methylation-specific PCR. Relative messenger RNA and the expression level of MTSS1 protein were identified by quantitative real-time PCR (qRT-PCR) and western blot analysis. The migration, invasion, proliferation, and cell cycle were detected separately by wound-healing assay, transwell chamber assay, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide assay and flow cytometry. The roles of MTSS1 in PCa progression were demonstrated in vivo by tumor formation assays in nude mice. MTSS1 expression was decreased in PCa tissues in comparison with paired adjacent normal prostate tissues. Compared to the methylation of MTSS1 in normal prostate tissues based on the MethHC website, the MTSS1 in PCa tissues was hypermethylated. The expression of MTSS1 detected by qRT-PCR and western blot analysis was found to be downregulated in PCa cells and tissues. The reduced expression of MTSS1 by small interfering RNA-MTSS1 was recovered by 5-aza-2'-deoxycytidine treatment. Besides, MTSS1 demethylation inhibited migration, invasion, and proliferation of PCa cells, and induced cell cycle to be arrested at G0/G1 phase. Furthermore, it was shown by tumor xenograft assay that MTSS1 inhibited the growth of tumor in vivo. Hypermethylated MTSS1 promoted PCa cells migration, invasion, and proliferation, and suppressed cell cycle arrest at the G0/G1 phase.

Methylation of global DNA repeat LINE-1 and subtelomeric DNA repeats D4Z4 in leukocytes is associated with biochemical recurrence in African American prostate cancer patients

Global DNA methylation may play important roles in cancer etiology and prognosis. The goal of this study is to investigate whether the methylation of long interspersed nucleotide elements (LINE-1) and subtelomeric DNA repeats D4Z4 in leukocyte DNA is associated with aggressive prostate cancer (PCa) in African Americans. We measured DNA methylation levels of LINE-1 and D4Z4 in 306 African American (AA) PCa patients using pyrosequencing and compared their methylation levels among clinical variables. We further applied multivariate Cox proportional hazards model and Kaplan-Meier survival function and log-rank tests to assess the association between DNA methylation and biochemical recurrence (BCR). Overall, there was no significant difference of the methylation levels of LINE-1 and D4Z4 among patients with different clinical and epidemiological characteristics. However, the methylation of LINE-1 and D4Z4 was associated with BCR. Patients with lower LINE-1 methylation and higher D4Z4 methylation exhibited markedly increased risks of BCR with adjusted hazard ratios of 3.34 (95% confidence interval, 1.32-8.45) and 4.12 (95% confidence interval, 1.32-12.86), respectively, and significantly shorter BCR-free survival times. Our results suggest that lower global DNA methylation and higher subtelomeric region methylation may predict worse prognosis in localized AA PCa patients.

Identification of key DNA methylation-driven genes in prostate adenocarcinoma: an integrative analysis of TCGA methylation data

Background

Prostate cancer (PCa) remains the second leading cause of deaths due to cancer in the United States in men. The aim of this study was to perform an integrative epigenetic analysis of prostate adenocarcinoma to explore the epigenetic abnormalities involved in the development and progression of prostate adenocarcinoma. The key DNA methylation-driven genes were also identified.

Methods

Methylation and RNA-seq data were downloaded for The Cancer Genome Atlas (TCGA). Methylation and gene expression data from TCGA were incorporated and analyzed using MethylMix package. Methylation data from the Gene Expression Omnibus (GEO) were assessed by R package limma to obtain differentially methylated genes. Pathway analysis was performed on genes identified by MethylMix criteria using ConsensusPathDB. Gene Ontology (GO) term enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were also applied for the identification of pathways in which DNA methylation-driven genes significantly enriched. The protein–protein interaction (PPI) network and module analysis in Cytoscape software were used to find the hub genes. Two methylation profile (GSE112047 and GSE76938) datasets were utilized to validate screened hub genes. Immunohistochemistry of these hub genes were evaluated by the Human Protein Atlas.

Results

A total of 553 samples in TCGA database, 32 samples in GSE112047 and 136 samples in GSE76938 were included in this study. There were a total of 266 differentially methylated genes were identified by MethylMix. Plus, a total of 369 differentially methylated genes and 594 differentially methylated genes were identified by the R package limma in GSE112047 and GSE76938, respectively. GO term enrichment analysis suggested that DNA methylation-driven genes significantly enriched in oxidation–reduction process, extracellular exosome, electron carrier activity, response to reactive oxygen species, and aldehyde dehydrogenase [NAD(P)+] activity. KEGG pathway analysis found DNA methylation-driven genes significantly enriched in five pathways including drug metabolism—cytochrome P450, phenylalanine metabolism, histidine metabolism, glutathione metabolism, and tyrosine metabolism. The validated hub genes were MAOB and RTP4.

Conclusions

Methylated hub genes, including MAOB and RTP4, can be regarded as novel biomarkers for accurate PCa diagnosis and treatment. Further studies are needed to draw more attention to the roles of these hub genes in the occurrence and development of PCa.

Genes Controlled by DNA Methylation Are Involved in Wilms Tumor Progression

To identify underlying mechanisms involved with metastasis formation in Wilms tumors (WTs), we performed comprehensive DNA methylation and gene expression analyses of matched normal kidney (NK), WT blastemal component, and metastatic tissues (MT) from patients treated under SIOP 2001 protocol. A linear Bayesian framework model identified 497 differentially methylated positions (DMPs) between groups that discriminated NK from WT, but MT samples were divided in two groups. Accordingly, methylation variance grouped NK and three MT samples tightly together and all WT with four MT samples that showed high variability. WT were hypomethylated compared to NK, and MT had a hypermethylated pattern compared to both groups. The methylation patterns were in agreement with methylases and demethylases expression. Methylation data pointed to the existence of two groups of metastases. While hierarchical clustering analysis based on the expression of all 2569 differentially expressed genes (DEGs) discriminated WT and MT from all NK samples, the hierarchical clustering based on the expression of 44 genes with a differentially methylated region (DMR) located in their promoter region revealed two groups: one containing all NKs and three MTs and one containing all WT and four MTs. Methylation changes might be controlling expression of genes associated with WT progression. The 44 genes are candidates to be further explored as a signature for metastasis formation in WT.

Molecular Mechanisms Related to Hormone Inhibition Resistance in Prostate Cancer

Management of metastatic or advanced prostate cancer has acquired several therapeutic approaches that have drastically changed the course of the disease. In particular due to the high sensitivity of prostate cancer cells to hormone depletion, several agents able to inhibit hormone production or binding to nuclear receptor have been evaluated and adopted in clinical practice. However, despite several hormonal treatments being available nowadays for the management of advanced or metastatic prostate cancer, the natural history of the disease leads inexorably to the development of resistance to hormone inhibition. Findings regarding the mechanisms that drive this process are of particular and increasing interest as these are potentially related to the identification of new targetable pathways and to the development of new drugs able to improve our patients' clinical outcomes.

Peptidase inhibitor 15 as a novel blood diagnostic marker for cholangiocarcinoma

BACKGROUND

We aimed to screen a specific secretory protein that could serve as blood diagnostic marker for cholangiocarcinoma (CCA).

METHODS

Starting with the analysis of gene expression profiles in tumor tissues and matched normal tissues from cases with CCA and hepatocellular carcinoma (HCC), we identified peptidase inhibitor 15 (PI15) was a potential diagnostic marker for CCA. We demonstrated PI15 expression levels in CCA, HCC, and normal liver tissues. Furthermore, quantitative enzyme-linked immunosorbent assay (ELISA) assessed plasma PI15 levels in CCA (n = 61), HCC (n = 72), benign liver disease (n = 28), chronic hepatitis B (CHB) patients (n = 45), and healthy individuals (n = 45). The diagnostic value of PI15 was estimated by the area under the receiver operating characteristic (ROC) curve (AUC).

FINDINGS

The positive rate of PI15 expression was 70% in CCA and only 9.1% in HCC; PI15 was not detected in normal liver tissue. High levels of plasma PI15 were evident in CCA patients, whereas only low levels were observed in cases involving HCC, benign liver disease, CHB patients, and healthy individuals. Plasma PI15 levels in CCA patients were obviously reduced (p = .0014) after surgery. The AUC of plasma PI15 for discriminating between CCA and HCC was 0.735. Furthermore, with a specificity of 94.44%, the combination of CA19-9 (>98.5 U/ml) and PI15 (>13 ng/ml) yielded a sensitivity of 80.39% for CCA and HCC.

INTERPRETATION

PI15 exhibits promise as a novel marker for predicting the diagnosis and follow-up of CCA patients. FUND: Natural Science Research Foundation of Anhui Province and Natural Science Foundation of China.

Vigorous Physical Activity Is Associated with Lower Risk of Metastatic-Lethal Progression in Prostate Cancer and Hypomethylation in the CRACR2A Gene

BACKGROUND

There is preliminary evidence linking physical activity to better prostate cancer outcomes, though the molecular mechanisms underlying this association are not clear.

METHODS

In a Seattle-based cohort of patients diagnosed with clinically localized prostate cancer and prospective follow-up for outcomes (n = 1,354), we studied the association between self-reported vigorous physical activity and prostate cancer progression to a metastatic-lethal phenotype. A subset of patients had prostate cancer tissue samples available for investigating DNA methylation (Infinium HumanMethylation450 BeadChip array) and exercise (n = 524).

RESULTS

Patients who had vigorous physical activity at least once per week during the year before diagnosis (∼79% of the cohort) were significantly less likely to progress to metastatic-lethal prostate cancer compared with those who had vigorous physical activity less frequently (adjusted hazard ratio = 0.63; P = 0.029). Among the subset of men who had radical prostatectomy as primary treatment and tumor tissue available, a differentially methylated region (DMR) was identified (family-wise error rate = 0.03, hypomethylated in the weekly exercise group), with 9 methylation probes located in the promoter region of CRACR2A. This gene encodes a calcium binding protein involved in innate immune response. The methylation level of the nine CpGs was inversely correlated with CRACR2A gene expression (average correlation coefficient = -0.35).

CONCLUSIONS

Vigorous physical activity before diagnosis is associated with epigenetic alterations of CRACR2A and prostate cancer metastatic-lethal progression.

IMPACT

This analysis provides strong evidence for the association between vigorous physical activity and a less likelihood to develop metastatic-lethal progression, and a suggestive link between exercise and DNA methylation in the CRACRA2A gene.

Phospholipid flippases enable precursor B cells to flee engulfment by macrophages

ATP11A and ATP11C, members of the P4-ATPases, are flippases that translocate phosphatidylserine (PtdSer) from the outer to inner leaflet of the plasma membrane. Using the W3 T lymphoma cell line, we found that Ca²⁺ ionophore-induced phospholipid scrambling caused prolonged PtdSer exposure in cells lacking both the ATP11A and ATP11C genes. ATP11C-null (ATP11C ^-/y ) mutant mice exhibit severe B-cell deficiency. In wild-type mice, ATP11C was expressed at all B-cell developmental stages, while ATP11A was not expressed after pro-B-cell stages, indicating that ATP11C ^-/y early B-cell progenitors lacked plasma membrane flippases. The receptor kinases MerTK and Axl are known to be essential for the PtdSer-mediated engulfment of apoptotic cells by macrophages. MerTK ^-/- and Axl ^-/- double deficiency fully rescued the lymphopenia in the ATP11C ^-/y bone marrow. Many of the rescued ATP11C ^-/y pre-B and immature B cells exposed PtdSer, and these cells were engulfed alive by wild-type peritoneal macrophages, in a PtdSer-dependent manner. These results indicate that ATP11A and ATP11C in precursor B cells are essential for rapidly internalizing PtdSer from the cell surface to prevent the cells' engulfment by macrophages.

Phenotype-independent DNA methylation changes in prostate cancer

Background

Human prostate cancers display numerous DNA methylation changes compared to normal tissue samples. However, definitive identification of features related to the cells’ malignant status has been compromised by the predominance of cells with luminal features in prostate cancers.

Methods

We generated genome-wide DNA methylation profiles of cell subpopulations with basal or luminal features isolated from matched prostate cancer and normal tissue samples.

Results

Many frequent DNA methylation changes previously attributed to prostate cancers are here identified as differences between luminal and basal cells in both normal and cancer samples. We also identified changes unique to each of the two cancer subpopulations. Those specific to cancer luminal cells were associated with regulation of metabolic processes, cell proliferation and epithelial development. Within the prostate cancer TCGA dataset, these changes were able to distinguish not only cancers from normal samples, but also organ-confined cancers from those with extraprostatic extensions. Using changes present in both basal and luminal cancer cells, we derived a new 17-CpG prostate cancer signature with high predictive power in the TCGA dataset.

Conclusions

This study demonstrates the importance of comparing phenotypically matched prostate cell populations from normal and cancer tissues to unmask biologically and clinically relevant DNA methylation changes.

A five-CpG DNA methylation score to predict metastatic-lethal outcomes in men treated with radical prostatectomy for localized prostate cancer

BACKGROUND

Prognostic biomarkers for localized prostate cancer (PCa) could improve personalized medicine. Our group previously identified a panel of differentially methylated CpGs in primary tumor tissue that predict disease aggressiveness, and here we further validate these biomarkers.

METHODS

Pyrosequencing was used to assess CpG methylation of eight biomarkers previously identified using the HumanMethylation450 array; CpGs with strongly correlated (r >0.70) results were considered technically validated. Logistic regression incorporating the validated CpGs and Gleason sum was used to define and lock a final model to stratify men with metastatic-lethal versus non-recurrent PCa in a training dataset. Coefficients from the final model were then used to construct a DNA methylation score, which was evaluated by logistic regression and Receiver Operating Characteristic (ROC) curve analyses in an independent testing dataset.

RESULTS

Five CpGs were technically validated and all were retained (P < 0.05) in the final model. The 5-CpG and Gleason sum coefficients were used to calculate a methylation score, which was higher in men with metastatic-lethal progression (P = 6.8 × 10-6 ) in the testing dataset. For each unit increase in the score there was a four-fold increase in risk of metastatic-lethal events (odds ratio, OR = 4.0, 95%CI = 1.8-14.3). At 95% specificity, sensitivity was 74% for the score compared to 53% for Gleason sum alone. The score demonstrated better prediction performance (AUC = 0.91; pAUC = 0.037) compared to Gleason sum alone (AUC = 0.87; pAUC = 0.025).

CONCLUSIONS

The DNA methylation score improved upon Gleason sum for predicting metastatic-lethal progression and holds promise for risk stratification of men with aggressive tumors. This prognostic score warrants further evaluation as a tool for improving patient outcomes.

Epigenome-wide SRC-1-Mediated Gene Silencing Represses Cellular Differentiation in Advanced Breast Cancer

Purpose: Despite the clinical utility of endocrine therapies for estrogen receptor-positive (ER) breast cancer, up to 40% of patients eventually develop resistance, leading to disease progression. The molecular determinants that drive this adaptation to treatment remain poorly understood. Methylome aberrations drive cancer growth yet the functional role and mechanism of these epimutations in drug resistance are poorly elucidated.Experimental Design: Genome-wide multi-omics sequencing approach identified a differentially methylated hub of prodifferentiation genes in endocrine resistant breast cancer patients and cell models. Clinical relevance of the functionally validated methyl-targets was assessed in a cohort of endocrine-treated human breast cancers and patient-derived ex vivo metastatic tumors.Results: Enhanced global hypermethylation was observed in endocrine treatment resistant cells and patient metastasis relative to sensitive parent cells and matched primary breast tumor, respectively. Using paired methylation and transcriptional profiles, we found that SRC-1-dependent alterations in endocrine resistance lead to aberrant hypermethylation that resulted in reduced expression of a set of differentiation genes. Analysis of ER-positive endocrine-treated human breast tumors (n = 669) demonstrated that low expression of this prodifferentiation gene set significantly associated with poor clinical outcome (P = 0.00009). We demonstrate that the reactivation of these genes in vitro and ex vivo reverses the aggressive phenotype.Conclusions: Our work demonstrates that SRC-1-dependent epigenetic remodeling is a 'high level' regulator of the poorly differentiated state in ER-positive breast cancer. Collectively these data revealed an epigenetic reprograming pathway, whereby concerted differential DNA methylation is potentiated by SRC-1 in the endocrine resistant setting. Clin Cancer Res; 24(15); 3692-703. ©2018 AACR.

Analysis of Tumor Biology to Advance Cancer Health Disparity Research

Cancer mortality rates in the United States continue to decline. Reductions in tobacco use, uptake of preventive measures, adoption of early detection methods, and better treatments have resulted in improved cancer outcomes for men and women. Despite this progress, some population groups continue to experience an excessive cancer burden when compared with other population groups. One of the most prominent cancer health disparities exists in prostate cancer. Prostate cancer mortality rates are highest among men of African ancestry when compared with other men, both in the United States and globally. This disparity and other cancer health disparities are largely explained by differences in access to health care, diet, lifestyle, cultural barriers, and disparate exposures to carcinogens and pathogens. Dietary and lifestyle factors, pathogens, and ancestry-related factors can modify tumor biology and induce a more aggressive disease. There are numerous examples of how environmental exposures, like tobacco, chronic stress, or dietary factors, induce an adverse tumor biology, leading to a more aggressive disease and decreased patient survival. Because of population differences in the exposure to these risk factors, they can be the cause of cancer disparities. In this review, we will summarize recent advances in our understanding of prostate and breast cancer disparities in the United States and discuss how the analysis of tumor biology can advance health disparity research.

Combining multiple biomarkers linearly to maximize the partial area under the ROC curve

It is now common in clinical practice to make clinical decisions based on combinations of multiple biomarkers. In this paper, we propose new approaches for combining multiple biomarkers linearly to maximize the partial area under the receiver operating characteristic curve (pAUC). The parametric and nonparametric methods that have been developed for this purpose have limitations. When the biomarker values for populations with and without a given disease follow a multivariate normal distribution, it is easy to implement our proposed parametric approach, which adopts an alternative analytic expression of the pAUC. When normality assumptions are violated, a kernel-based approach is presented, which handles multiple biomarkers simultaneously. We evaluated the proposed as well as existing methods through simulations and discovered that when the covariance matrices for the disease and nondisease samples are disproportional, traditional methods (such as the logistic regression) are more likely to fail to maximize the pAUC while the proposed methods are more robust. The proposed approaches are illustrated through application to a prostate cancer data set, and a rank-based leave-one-out cross-validation procedure is proposed to obtain a realistic estimate of the pAUC when there is no independent validation set available.

Molecular determinants of prostate cancer metastasis

Metastatic cancer remains largely incurable and fatal. The general course of cancer, from the initiation of primary tumor formation and progression to metastasis, is a multistep process wherein tumor cells at each step must display specific phenotypic features. Distinctive capabilities required for primary tumor initiation and growth form the foundation, and sometimes may remain critical, for subsequent metastases. These phenotypic features must remain easily malleable during the acquisition of additional capabilities unique and essential to the metastatic process such as dissemination to distant tissues wherein tumor cells interact with foreign microenvironments. Thus, the metastatic phenotype is a culmination of multiple genetic and epigenetic alterations and subsequent selection for favorable traits under the pressure of ever-changing tumor microenvironments. Although our understanding of the molecular programs that drive cancer metastasis are incomplete, increasing evidence suggests that successful metastatic colonization relies on the dissemination of cancer stem cells (CSCs) with tumor-regenerating capacity and adaptive programs for survival in distant organs. In the past 2-3 years, a myriad of novel molecular regulators and determinants of prostate cancer metastasis have been reported, and in this Perspective, we comprehensively review this body of literature and summarize recent findings regarding cell autonomous molecular mechanisms critical for prostate cancer metastasis.

PTEN loss is associated with prostate cancer recurrence and alterations in tumor DNA methylation profiles

BACKGROUND

Prostate cancer (PCa) with loss of the tumor suppressor gene PTEN has an unfavorable prognosis. DNA methylation profiles associated with PTEN loss may provide further insights into the mechanisms underlying these more aggressive, clinically relevant tumors.

METHODS

The cohort included patients with clinically localized PCa. Samples taken from the primary tumor were used to determine PTEN genomic deletions using FISH, and to analyze epigenome-wide DNA methylation profiles. Patients were followed for PCa recurrence on average for 8 years after diagnosis.

RESULTS

The study included 471 patients with data on PTEN loss, and the frequency of hemi- and homozygous PTEN loss was 10.0% and 4.5%, respectively. Loss of PTEN was associated with a significantly higher risk of recurrence (any vs. no PTEN loss; HR = 1.74; 95% CI: 1.03-2.93). Hazard ratios for hemi- and homozygous loss were 1.39 (95% CI: 0.73-2.64) and 2.84 (95% CI: 1.30-6.19), respectively. Epigenome-wide methylation profiling identified 4,208 differentially methylated CpGs (FDR Q-value < 0.01) in tumors with any versus no PTEN loss. There were no genome-wide significant differentially methylated CpGs in homo- versus hemizygous deleted tumors. Tumor methylation data were used to build a methylation signature of PTEN loss in our cohort, which was confirmed in TCGA, and included CpGs in ATP11A, GDNF, JAK1, JAM3, and VAPA.

CONCLUSION

Loss of PTEN was positively associated with PCa recurrence. Prostate tumors with PTEN loss harbor a distinct methylation signature, and these aberrantly methylated CpG sites may mediate tumor progression when PTEN is deleted.

Stromal and epithelial transcriptional map of initiation progression and metastatic potential of human prostate cancer

While progression from normal prostatic epithelium to invasive cancer is driven by molecular alterations, tumor cells and cells in the cancer microenvironment are co-dependent and co-evolve. Few human studies to date have focused on stroma. Here, we performed gene expression profiling of laser capture microdissected normal non-neoplastic prostate epithelial tissue and compared it to non-transformed and neoplastic low-grade and high-grade prostate epithelial tissue from radical prostatectomies, each with its immediately surrounding stroma. Whereas benign epithelium in prostates with and without tumor were similar in gene expression space, stroma away from tumor was significantly different from that in prostates without cancer. A stromal gene signature reflecting bone remodeling and immune-related pathways was upregulated in high compared to low-Gleason grade cases. In validation data, the signature discriminated cases that developed metastasis from those that did not. These data suggest that the microenvironment may influence prostate cancer initiation, maintenance, and metastatic progression.Stromal cells contribute to tumor development but the mechanisms regulating this process are still unclear. Here the authors analyze gene expression profiles in the prostate and show that stromal gene signature changes ahead of the epithelial gene signature as prostate cancer initiates and progresses.

Identification of prognostic markers of high grade prostate cancer through an integrated bioinformatics approach

PURPOSE

Prostate cancer is one of the leading causes of cancer death for male. In the present study, we applied an integrated bioinformatics approach to provide a novel perspective and identified some hub genes of prostate cancer.

METHOD

Microarray data of fifty-nine prostate cancer were downloaded from Gene Expression Omnibus. Gene Ontology and pathway analysis were applied for differentially expressed genes between high and low grade prostate cancer. Weighted gene coexpression network analysis was applied to construct gene network and classify genes into different modules. The most related module to high grade prostate cancer was identified and hub genes in the module were revealed. Ingenuity pathway analysis was applied to check the chosen module's relationship to high grade prostate cancer. Hub gene's expression profile was verified with clinical samples and a dataset from The Cancer Genome Atlas project.

RESULT

3193 differentially expressed genes were filtered and gene ontology and pathway analysis revealed some cancer- and sex hormone-related results. Weighted gene coexpression network was constructed and genes were classified into six modules. The red module was selected and ingenuity pathway analysis confirmed its relationship with high grade prostate cancer. Hub genes were identified and their expression profile was also confirmed.

CONCLUSION

The present study applied integrate bioinformatics approaches to generate a holistic view of high grade prostate cancer and identified hub genes could serve as prognosis markers and potential treatment targets.

The association between RARβ and FHIT promoter methylation and the carcinogenesis of patients with cervical carcinoma: A meta-analysis

The RARβ and FHIT promoter methylation are observed in some cervical carcinoma. However, the association between RARβ and FHIT promoter methylation and cervical carcinogenesis remains unclear. This study was carried out to evaluate the correlation between RARβ or FHIT promoter methylation and cervical carcinogenesis. Eligible publications were searched via online databases. The combined odds ratios and corresponding 95% confidence intervals were calculated and summarized. In all, 17 eligible articles on RARβ and FHIT promoter methylation were identified in the study. RARβ promoter methylation was significantly higher in cervical cancer than in cervical intraepithelial neoplasia lesions and normal cervical tissues (odds ratio = 3.90, p = 0.018; odds ratio = 12.98, p < 0.001, respectively). There was more FHIT promoter methylation in cervical cancer than in cervical intraepithelial neoplasia lesions and normal controls (odds ratio = 8.0, p = 0.055; odds ratio = 10.75, p < 0.001, respectively). In addition, FHIT promoter methylation was correlated with clinical stage (advanced stage vs early stage: odds ratio = 2.69, p = 0.056) and tumor grade (high grade vs low grade: odds ratio = 4.11, p < 0.001). RARβ and FHIT promoter methylation may be associated with the carcinogenesis of cervical cancer. FHIT promoter methylation may play a crucial role in cervical cancer progression. Additional studies with large sample sizes are essential to confirm our findings.