Identification of the subtypes of gastric cancer based on DNA methylation and the prediction of prognosis

Deep Clustering-Based Metabolic Stratification of Non-Small Cell Lung Cancer Patients Through Integration of Somatic Mutation Profile and Network Propagation Algorithm

As a common malignancy of the lower respiratory tract, non-small cell lung cancer (NSCLC) represents a major oncological challenge globally, characterized by high incidence and mortality rates. Recent research highlights the critical involvement of somatic mutations in the onset and development of NSCLC. Stratification of NSCLC patients based on somatic mutation data could facilitate the identification of patients likely to respond to personalized therapeutic strategies. However, stratification of NSCLC patients using somatic mutation data is challenging due to the sparseness of this data. In this study, based on sparse somatic mutation data from 4581 NSCLC patients from the Memorial Sloan Kettering Cancer Center (MSKCC) database, we systematically evaluate the metabolic pathway activity in NSCLC patients through the application of network propagation algorithm and computational biology algorithms. Based on these metabolic pathways associated with prognosis, as recognized through univariate Cox regression analysis, NSCLC patients are stratified using the deep clustering algorithm to explore the optimal classification strategy, thereby establishing biologically meaningful metabolic subtypes of NSCLC patients. The precise NSCLC metabolic subtypes obtained from the network propagation algorithm and deep clustering algorithm are systematically evaluated and validated for survival benefits of immunotherapy. Our research marks progress towards developing a universal approach for classifying NSCLC patients based solely on somatic mutation profiles, employing deep clustering algorithm. The implementation of our research will help to deepen the analysis of NSCLC patients' metabolic subtypes from the perspective of tumor microenvironment, providing a strong basis for the formulation of more precise personalized treatment plans.

Novel DNA methylation markers for early detection of gastric cardia adenocarcinoma and esophageal squamous cell carcinoma

Gastric cardia adenocarcinoma (GCA) and esophageal squamous cell carcinoma (ESCC) present significant health challenges in China, often diagnosed at advanced stages with poor prognoses. However, effective biomarkers for early detection remain elusive. This study aimed to integrate methylome and transcriptome data to identify DNA methylation markers for the early detection of GCA and ESCC. In the discovery stage, we conducted Infinium MethylationEPIC array analysis on 36 paired GCA and non-tumor adjacent tissues (NAT), identifying differentially methylated CpG sites (DMCs) between GCA/ESCC and NAT through combined analyses of in-house and publicly available data. In the validation stage, targeted pyrosequencing and quantitative real-time RT-PCR were performed on paired tumor and NAT samples from 50 GCA and 50 ESCC patients. In the application stage, an independent set of 438 samples, including GCA, ESCC, high- and low-grade dysplasia (HGD/LGD), and normal controls, was tested for selected DMCs using pyrosequencing. Our analysis validated three GCA-specific, two ESCC-specific, and one tumor-shared DMCs, exhibiting significant hypermethylation and decreased expression of target genes in tumor samples compared with NAT. Leveraging these DMCs, we developed a GCA-specific 4-marker panel (cg27284428, cg11798358, cg07880787, and cg00585116) with an area under the receiver operating characteristic curve (AUC) of 0.917, effectively distinguishing between cardia HGD/GCA patients and cardia LGD/normal controls. Similarly, an ESCC-specific 3-marker panel (cg14633892, cg04415798, and cg00585116) achieved an AUC of 0.865 in distinguishing esophageal HGD/ESCC cases. Furthermore, integrating cg00585116, age, and alcohol consumption yielded a tumor-shared logistic model with good discrimination for two cancer/HGD (AUC, 0.767; 95% confidence interval, 0.720-0.813). The mean AUC of the model after 5-fold cross-validation was 0.764. In summary, our study identifies novel DNA methylation markers capable of accurately distinguishing GCA/ESCC and HGD from LGD and normal controls. These findings offer promising prospects for targeted DNA methylation assays in future minimally invasive cancer screening methods.

Genome-wide DNA methylation profiling reveals a novel hypermethylated biomarker PRKCB in gastric cancer

Globally, gastric cancer (GC) ranks among the most prevalent forms of malignancy, posing a significant health burden. Epigenetic modifications, predominantly characterized by alterations in DNA methylation patterns, have been linked to a diverse array of neoplastic processes. Here, we undertake a comprehensive analysis of the DNA methylation signature in GC, with the aim to discover the potential diagnostic epigenetic biomarkers. Utilizing the Illumina 935 K BeadChip, we conducted a genome-wide exploration of DNA methylation patterns in four paired samples of GC tissues and adjacent non-cancerous counterparts. The bisulfite-pyrosequencing (n = 7) was employed to the quantification for methylated gene. The pubic databases including GWAS Catalog, TCGA and GEO were used. The immunohistochemistry and qRT-PCR analysis were performed. In contrast to adjacent tissues, GC tissues manifested pronounced hypermethylation patterns specifically within the promoter cytosine-phosphate-guanine (CpG) islands, indicating localized epigenetic alterations. DNA methylome analysis further revealed 4432 differentially-methylated probes (DMPs), with the gene PRKCB exhibited the most prominent average DNA methylation disparity (mean Δβ = 0.353). Pyrosequencing validation confirmed three DMPs within the PRKCB promoter (cg08406370, cg00735962, and cg18526361). Notably, the mean methylation levels of PRKCB were inversely correlated with mRNA expression levels in the GWAS Catalog. Furthermore, both mRNA and protein expression levels of PRKCB were significantly reduced in GCs when compared to their adjacent non-cancerous counterparts, verified by TCGA and GEO database. Our study reveals significant DNA methylation alterations in GC and emphasizes the pivotal role of PRKCB gene hypermethylation in conferring GC risk, which offers fresh perspectives for advancing diagnostic approaches and therapeutic strategies for GC.

Comprehensive Multiomics Analyses Establish the Optimal Prognostic Model for Resectable Gastric Cancer : Prognosis Prediction for Resectable GC

BACKGROUND

Prognostic models based on multiomics data may provide better predictive capability than those established at the single-omics level. Here we aimed to establish a prognostic model for resectable gastric cancer (GC) with multiomics information involving mutational, copy number, transcriptional, methylation, and clinicopathological alterations.

PATIENTS AND METHODS

The mutational, copy number, transcriptional, methylation data of 268, 265, 226, and 252 patients with stages I-III GC were downloaded from the TCGA database, respectively. Alterations from all omics were characterized, and prognostic models were established at the individual omics level and optimized at the multiomics level. All models were validated with a cohort of 99 patients with stages I-III GC.

RESULTS

TTN, TP53, and MUC16 were among the genes with the highest mutational frequency, while UBR5, ZFHX4, PREX2, and ARID1A exhibited the most prominent copy number variations (CNVs). Upregulated COL10A1, CST1, and HOXC10 and downregulated GAST represented the biggest transcriptional alterations. Aberrant methylation of some well-known genes was revealed, including CLDN18, NDRG4, and SDC2. Many alterations were found to predict the patient prognosis by univariate analysis, while four mutant genes, two CNVs, five transcriptionally altered genes, and seven aberrantly methylated genes were identified as independent risk factors in multivariate analysis. Prognostic models at the single-omics level were established with these alterations, and optimized combination of selected alterations with clinicopathological factors was used to establish a final multiomics model. All single-omics models and the final multiomics model were validated by an independent cohort. The optimal area under the curve (AUC) was 0.73, 0.71, 0.71, and 0.85 for mutational, CNV, transcriptional, and methylation models, respectively. The final multiomics model significantly increased the AUC to 0.92 (P < 0.05).

CONCLUSIONS

Multiomics model exhibited significantly better capability in predicting the prognosis of resectable GC than single-omics models.

Discovery of novel DNA methylation biomarker panels for the diagnosis and differentiation between common adenocarcinomas and their liver metastases

Differentiation between adenocarcinomas is sometimes challenging. The promising avenue for discovering new biomarkers lies in bioinformatics using DNA methylation analysis. Utilizing a 2853-sample identification dataset and a 782-sample independent verification dataset, we have identified diagnostic DNA methylation biomarkers that are hypermethylated in cancer and differentiate between breast invasive carcinoma, cholangiocarcinoma, colorectal cancer, hepatocellular carcinoma, lung adenocarcinoma, pancreatic adenocarcinoma and stomach adenocarcinoma. The best panels for cancer type exhibit sensitivity of 77.8-95.9%, a specificity of 92.7-97.5% for tumors, a specificity of 91.5-97.7% for tumors and normal tissues and a diagnostic accuracy of 85.3-96.4%. We have shown that the results can be extended from the primary cancers to their liver metastases, as the best panels diagnose and differentiate between pancreatic adenocarcinoma liver metastases and breast invasive carcinoma liver metastases with a sensitivity and specificity of 83.3-100% and a diagnostic accuracy of 86.8-91.9%. Moreover, the panels could detect hypermethylation of selected regions in the cell-free DNA of patients with liver metastases. At the same time, these were unmethylated in the cell-free DNA of healthy donors, confirming their applicability for liquid biopsies.

Downregulation of Ambra1 by altered DNA methylation exacerbates dopaminergic neuron damage in a fenpropathrin-induced Parkinson-like mouse model

Fenpropathrin (Fen), a volatile pyrethroid insecticide, is used widely for agricultural applications and has been reported to increase the risk of Parkinson's disease (PD). However, the molecular basis, underlying mechanisms, and pathophysiology of Fen-exposed Parkinsonism remain unknown. Recent studies have revealed epigenetic mechanisms underlying PD-related pathway regulation, including DNA methylation. Epigenetic mechanisms are potential targets for therapeutic intervention in neurodegenerative diseases. After whole-genome bisulfite sequencing (WGBS) of midbrain tissues from a Fen-exposed PD-like mouse model, we performed an association analysis of DNA methylation and gene expression. Then we successfully screened for the DNA methylation differential gene Ambra1, which is closely related to PD. The hypermethylation-low expression Ambra1 gene aggravated DA neuron damage in vitro and in vivo through the Ambra1/Parkin/LC3B-mediated mitophagy pathway. We administered 5-aza-2'-deoxycytidine (5-Aza-dC) to upregulate Ambra1 expression, thereby reducing Ambra1-mediated mitophagy and protecting DA neurons against Fen-induced damage. In conclusion, these findings elucidate the potential function of Ambra1 under the regulation of DNA methylation, suggesting that the inhibition of DNA methylation may alleviate Fen-exposed neuron damage.

Focusing on the Abnormal Events of NPC1, NPC2, and NPC1L1 in Pan-Cancer and Further Constructing LUAD and KICH Prediction Models

Cancer's high incidence and death rate jeopardize human health and life, and it has become a global public health issue. Some members of NPCs have been studied in a few cancers, but comprehensive and prognostic analysis is lacking in most cancers. In this study, we used the Cancer Genome Atlas (TCGA) data genomics and transcriptome technology to examine the differential expression and prognosis of NPCs in 33 cancer samples, as well as to investigate NPCs mutations and their effect on patient prognosis and to evaluate the methylation level of NPCs in cancer. The linked mechanisms and medication resistance were subsequently investigated in order to investigate prospective tumor therapy approaches. The relationships between NPCs and immune infiltration, immune cells, immunological regulatory substances, and immune pathways were also investigated. Finally, the LUAD and KICH prognostic prediction models were built using univariate and multivariate COX regression analysis. Additionally, the mRNA and protein levels of NPCs were also identified.

Host Transcriptional Regulatory Genes and Microbiome Networks Crosstalk through Immune Receptors Establishing Normal and Tumor Multiomics Metafirm of the Oral-Gut-Lung Axis

The microbiome has shown a correlation with the diet and lifestyle of each population in health and disease, the ability to communicate at the cellular level with the host through innate and adaptative immune receptors, and therefore an important role in modulating inflammatory process related to the establishment and progression of cancer. The oral cavity is one of the most important interaction windows between the human body and the environment, allowing the entry of an important number of microorganisms and their passage across the gastrointestinal tract and lungs. In this review, the contribution of the microbiome network to the establishment of systemic diseases like cancer is analyzed through their synergistic interactions and bidirectional crosstalk in the oral-gut-lung axis as well as its communication with the host cells. Moreover, the impact of the characteristic microbiota of each population in the formation of the multiomics molecular metafirm of the oral-gut-lung axis is also analyzed through state-of-the-art sequencing techniques, which allow a global study of the molecular processes involved of the flow of the microbiota environmental signals through cancer-related cells and its relationship with the establishment of the transcription factor network responsible for the control of regulatory processes involved with tumorigenesis.

Prognostic significance and immune characteristics of GPR27 in gastric cancer

Gastric cancer (GC) is one of the most typical cancerous neoplasms occurring in the digestive system. For advanced GC, immunotherapy is the final option for them to prolong survival time. Hence, we aimed to identify new molecular targets to enhance the immunotherapy response in GC individuals. Then we applied bioinformatic analysis to explore the expression profiles of G-protein-coupled receptor 27 (GPR27) transcription and GPR27 methylation. The associations between survival of GC patients and GPR27 transcription and methylation were then analyzed. We also studied the link between GPR27 expression and levels of immune cell infiltration. Finally, we gained insights into the prognostic role of GPR27 protein in 97 cases of GC individuals. According to datasets gained from TCGA, GPR27 mRNA is expressed lower in GC tissues. Down-regulation of GPR27 transcription was related with better survival in GC individuals, and GPR27 cg03024619 had the most significant prognostic value (HR=0.553, P<0.0001). In addition, the expression level of GPR27 has a clear interaction with immune cells' infiltration and their markers. Single-cell analysis displayed that GPR27 is mainly expressed in macrophages. Finally, down-regulation of GPR27 protein was observed in GC tissues and correlated with better survival outcomes. GPR27 can serve as an important prognostic biomarker and exert an immunomodulatory role in GC. Our findings highlight the significance of GPR27 in a variety of cancers, including GC, and provide clues for a better understanding of GPR27 from bioinformatics and clinically validated perspective.

Machine learning in the identification of prognostic DNA methylation biomarkers among patients with cancer: A systematic review of epigenome-wide studies

BACKGROUND

DNA methylation biomarkers have great potential in improving prognostic classification systems for patients with cancer. Machine learning (ML)-based analytic techniques might help overcome the challenges of analyzing high-dimensional data in relatively small sample sizes. This systematic review summarizes the current use of ML-based methods in epigenome-wide studies for the identification of DNA methylation signatures associated with cancer prognosis.

METHODS

We searched three electronic databases including PubMed, EMBASE, and Web of Science for articles published until 2 January 2023. ML-based methods and workflows used to identify DNA methylation signatures associated with cancer prognosis were extracted and summarized. Two authors independently assessed the methodological quality of included studies by a seven-item checklist adapted from 'A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies (PROBAST)' and from the 'Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK). Different ML methods and workflows used in included studies were summarized and visualized by a sunburst chart, a bubble chart, and Sankey diagrams, respectively.

RESULTS

Eighty-three studies were included in this review. Three major types of ML-based workflows were identified. 1) unsupervised clustering, 2) supervised feature selection, and 3) deep learning-based feature transformation. For the three workflows, the most frequently used ML techniques were consensus clustering, least absolute shrinkage and selection operator (LASSO), and autoencoder, respectively. The systematic review revealed that the performance of these approaches has not been adequately evaluated yet and that methodological and reporting flaws were common in the identified studies using ML techniques.

CONCLUSIONS

There is great heterogeneity in ML-based methodological strategies used by epigenome-wide studies to identify DNA methylation markers associated with cancer prognosis. In theory, most existing workflows could not handle the high multi-collinearity and potentially non-linearity interactions in epigenome-wide DNA methylation data. Benchmarking studies are needed to compare the relative performance of various approaches for specific cancer types. Adherence to relevant methodological and reporting guidelines are urgently needed.

Cancer functional states-based molecular subtypes of gastric cancer

BACKGROUND

The treatment of gastric cancer remains a challenge.

METHODS

We divided gastric cancer into three subtypes based on 14 cancer functional states. We investigated differences between subtypes through multi-omics data, especially at the single-cell level, which allowed us to analyze differences from the perspective of each type of cell rather than the whole.

RESULTS

The cluster 1 is characterized by high levels of tumor progression-related cancer functional status, worst survival outcomes, low metabolic level, high infiltration of immunosuppressive cells, high copy number variations (CNV), and low tumor mutational burden (TMB). The cluster 2 is characterized by low levels of tumor progression-related cancer functional status, favorable prognosis, moderate metabolic level, low immune cell infiltration, high CNV, and moderate TMB. Then, the cluster 3 is characterized by the high level of all cancer functional status, high metabolic level, low CNV, high TMB, high infiltration of immune cells with high cytotoxicity, and better response to immunotherapy. We also established a prognostic model based on cancer functional status and validated its robustness.

CONCLUSIONS

Collectively, our study identified gastric cancer subtypes and provided new insights into the clinical treatment of gastric cancer.

Identification of Melanoma Subsets Based on DNA Methylation Sites and Construction of a Prognosis Evaluation Model

Background

Melanoma is a lethal skin malignant tumor, and its formation or development is regulated by various genetic and epigenetic molecules. Although there are traditional methods provided for the doctors to evaluate the patients' prognosis or make the diagnosis, the novel method based on epigenetic markers is still needed to make the early diagnosis.

Results

We identified 256 melanoma-independent prognosis-related methylation sites (P < 0.0001) and divided patients into seven methylation subgroups. Methylation levels and survival time in the C2 subgroup were lower than that of other clusters (P < 0.05). We established the predicted model of prognosis risk for melanoma using the significantly changed methylation sites in C2. The model efficiently divided patients into high- and low-risk groups (area under the receiver operating characteristic curve, 0.833). Risk scores and patient survival time were negatively correlated (r_s = −0.325, P < 0.0001). Genes corresponding to the independent prognosis-associated methylation sites were enriched in cancer- and immunology-related pathways. We identified 35 hub genes. DOK2, GBP4, PSMB9, and NLRC5 were significantly changed according to methylation subgroups, survival, tumor stages, and T categories and were positively correlated, which was validated in the testing group (P < 0.05). The levels of DOK2, GBP4, PSMB9, and NLRC5 had an opposite trend to their methylation sites in patients with poor prognosis.

Conclusions

We identified seven DNA methylation subtypes and constructed a highly effective prognosis risk assessment model. The transcript levels of key genes corresponding to the independent prognosis-related methylation sites were significantly changed in patients according to prognosis and positively correlated with each other, indicating they may collaboratively promote melanoma formation. These findings further our understanding of the mechanism of melanoma and provide new targets for diagnosis and treatment.

Deep Learning based on hematoxylin-eosin staining outperforms immunohistochemistry in predicting molecular subtypes of gastric adenocarcinoma

In gastric cancer (GC), there are four molecular subclasses that indicate whether patients respond to chemotherapy or immunotherapy, according to the TCGA. In clinical practice, however, not every patient undergoes molecular testing. Many laboratories have used well-implemented in situ techniques (IHC and EBER-ISH) to determine the subclasses in their cohorts. Although multiple stains are used, we show that a staining approach is unable to correctly discriminate all subclasses. As an alternative, we trained an ensemble convolutional neuronal network using bagging that can predict the molecular subclass directly from hematoxylin-eosin histology. We also identified patients with predicted intra-tumoral heterogeneity or with features from multiple subclasses, which challenges the postulated TCGA-based decision tree for GC subtyping. In the future, Deep Learning may enable targeted testing for molecular subtypes and targeted therapy for a broader group of GC patients. This article is protected by copyright. All rights reserved.

Differential DNA Methylation and Gene Expression Between ALV-J-Positive and ALV-J-Negative Chickens

Background: Avian leukosis virus subgroup J (ALV-J) is an oncogenic virus that causes serious economic losses in the poultry industry; unfortunately, there is no effective vaccine against ALV-J. DNA methylation plays a crucial role in several biological processes, and an increasing number of diseases have been proven to be related to alterations in DNA methylation. In this study, we screened ALV-J-positive and -negative chickens. Subsequently, we generated and provided the genome-wide gene expression and DNA methylation profiles by MeDIP-seq and RNA-seq of ALV-J-positive and -negative chicken samples; 8,304 differentially methylated regions (DMRs) were identified by MeDIP-seq analysis (p ≤ 0.005) and 515 differentially expressed genes were identified by RNA-seq analysis (p ≤ 0.05). As a result of an integration analysis, we screened six candidate genes to identify ALV-J-negative chickens that possessed differential methylation in the promoter region. Furthermore, TGFB2 played an important role in tumorigenesis and cancer progression, which suggested TGFB2 may be an indicator for identifying ALV-J infections.

Immunological Significance of Prognostic DNA Methylation Sites in Hepatocellular Carcinoma

Background: Hepatocellular carcinoma (HCC) is a tumor with high morbidity and high mortality worldwide. DNA methylation, one of the most common epigenetic changes, might serve a vital regulatory role in cancer.

Methods: To identify categories based on DNA methylation data, consensus clustering was employed. The risk signature was yielded by systematic bioinformatics analyses based on the remarkably methylated CpG sites of cluster 1. Kaplan–Meier analysis, variable regression analysis, and ROC curve analysis were further conducted to validate the prognosis predictive ability of risk signature. Gene set enrichment analysis (GSEA) was performed for functional annotation. To uncover the context of tumor immune microenvironment (TIME) of HCC, we employed the ssGSEA algorithm and CIBERSORT method and performed TIMER database exploration and single-cell RNA sequencing analysis. Additionally, quantitative real-time polymerase chain reaction was employed to determine the LRRC41 expression and preliminarily explore the latent role of LRRC41 in prognostic prediction. Finally, mutation data were analyzed by employing the “maftools” package to delineate the tumor mutation burden (TMB).

Results: HCC samples were assigned into seven subtypes with different overall survival and methylation levels based on 5′-cytosine-phosphate-guanine-3′ (CpG) sites. The risk prognostic signature including two candidate genes (LRRC41 and KIAA1429) exhibited robust prognostic predictive accuracy, which was validated in the external testing cohort. Then, the risk score was significantly correlated with the TIME and immune checkpoint blockade (ICB)–related genes. Besides, a prognostic nomogram based on the risk score and clinical stage presented powerful prognostic ability. Additionally, LRRC41 with prognostic value was corroborated to be closely associated with TIME characterization in both expression and methylation levels. Subsequently, the correlation regulatory network uncovered the potential targets of LRRC41 and KIAA1429. Finally, the methylation level of KIAA1429 was correlated with gene mutation status.

Conclusion: In summary, this is the first to identify HCC samples into distinct clusters according to DNA methylation and yield the CpG-based prognostic signature and quantitative nomogram to precisely predict prognosis. And the pivotal player of DNA methylation of genes in the TIME and TMB status was explored, contributing to clinical decision-making and personalized prognosis monitoring of HCC.