Specific breast cancer prognosis-subtype distinctions based on DNA methylation patterns

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

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

Abnormal methylation of HOXA11 promoter promotes tumor progression in testicular germ cell tumor

OBJECTIVE

To investigate the methylation of HOXA11 gene promoter in testicular germ cell tumor (GCT).

METHOD

The clinicopathological data of 63 patients with primary testicular GCT who underwent surgery during Apr. 2019 to Mar. 2021, were retrospectively analyzed. Their GCT tissue and paraneoplastic testicular tissue were obtained, and genomic DNA was extracted from both. The methylation of HOXA11 gene promoter region was detected by methylation-specific PCR (MSP). The incidence of HOXA11 methylation in testicular GCT and adjacent tissues was compared, and the connection between methylation level in testicular GCT and clinicopathologic features of patients was statistically analyzed. Testicular GCT cells were treated with methylated transferase inhibitor 5-Aza-dC in vitro, and HOXA11 mRNA expression was detected by real-time PCR.

RESULTS

The positive rate of HOXA11 promoter methylation in testicular GCT tissues was notably higher than that of paired adjacent tissues (P<0.05). The abnormal methylation of HOXA11 gene promoter was correlated with lymph node metastasis and TNM stage in patients (P<0.05). HOXA11 mRNA expression in testicular GCT cells treated with 5-Aza-dC was increased (P<0.05).

CONCLUSION

Abnormal methylation of HOXA11 gene promoter in testicular germ cell tumor tissue inhibits transcription and expression of HOXA11 gene. The abnormal methylation of HOXA11 promoter region is tightly associated with lymph node metastasis and TNM staging in testicular germ cell tumors.

Genome-wide analyses reveal the regulatory roles of DNA methylation-regulated alternative promoter transcripts in breast cancer

A certain proportion of genes are regulated by multiple, distinct promoters, revealing a dynamic landscape of the cancer transcriptome. However, the contribution of alternative promoters (APs) in breast cancer (BRCA) remains largely unexplored. Here, we identified 3654 genes with multiple promoters in BRCA patients, and 53 of them could generate distinct AP transcripts that are dysregulated and prognosis-related in BRCA, namely prognosis-related dysregulated AP (prdeAP) transcripts. Interestingly, when we searched for the genomic signatures of these prdeAP genes, we found that the promoter regions of 92% of the prdeAP genes were enriched with abundant DNA methylation signals. Through further bioinformatic analysis and experimental validation, we showed that AP selections of TANK, UNKL, CCL28, and MAP1LC3A were regulated by DNA methylation upon their corresponding promoter regions. Functionally, by overexpressing AP variants of TANK, we found that TANK|55731 could dramatically suppress MDA-MB-231 cell proliferation and migration. Meanwhile, pan-cancer survival analyses suggested that AP variants of TANK provided more accurate prognostic predictive ability than TANK gene in a variety of tumor types, including BRCA. Together, by uncovering the DNA methylation-regulated AP transcripts with tumor prognostic features, our work revealed a novel layer of regulators in BRCA progression and provided potential targets that served as effective biomarkers for anti-BRCA treatment.

The role of dietary polyphenols in alternating DNA methylation in cancer

Natural products such as curcumin, quercetin, and resveratrol have been shown to have antitumor effectsand several studies have examined their role in treating cancer, either alone or in combination with other chemotherapeutic drugs. These compounds are capable of affecting different cancer-related mechanisms, such as proliferation, inflammation, invasion, and metastasis. Along with all of the benefits of these agents, affecting epigenetic processes is one of the most important aspects of their impact. Epigenetic modifications can be categorized into three main processes that include DNA methylation, histone modification, and regulation of small non-coding RNAs. Therefore, targeting DNA methylation can be used as a cancer treatment strategy by identifying or developing methylation modulators. Herein, we take a look into the studies investigating the role of natural products (e.g. curcumin, resveratrol, epigallocatechin gallate (EGCG), and quercetin) in alternating the DNA methylation status of various cancer cells. We discuss how these compounds reduce the expression of enzymes mediating the methylation of tumor suppressor genes and thereby, increasing the expression of tumor suppressors while reactivating antitumor signaling pathways.

SLC31A1 Identifying a Novel Biomarker with Potential Prognostic and Immunotherapeutic Potential in Pan-Cancer

Solute carrier family 31 member 1 (SLC31A1) encodes a protein that functions as a homotrimer for the uptake of dietary copper. As a vital member of the cuproptosis gene family, it plays an essential role in both normal tissues and tumors. In this study, we analyzed SLC31A1 across human cancer types to gain a better understanding of SLC31A1's role in cancer development. We searched for information using online databases to analyze, systematically and comprehensively, the role of SLC31A1 in tumors. Amongst nine cancer types, the expression of SLC31A1 was significantly different between tumors and normal tissues. According to further analysis, pancreatic cancer had the highest mutation rate of the SLC31A1 gene, and the methylation levels of the gene were significantly reduced in seven tumors. The expression of SLC31A1 is also linked to the infiltration of tumors by immune cells, the expression of immune checkpoint genes, and immunotherapy markers (TMB and MSI), suggesting that SLC31A1 may be of particular relevance in immunotherapy. This thorough analysis of SLC31A1 across different types of cancer gives us a clear and comprehensive insight into its role in causing cancer on a systemic level.

Precise subtyping reveals immune heterogeneity for hormone receptor-positive breast cancer

A significant proportion of breast cancer cases are characterized by hormone receptor positivity (HR+). Clinically, the heterogeneity of HR+ breast cancer leads to different therapeutic effects on endocrine. Therefore, definition of subgroups in HR+ breast cancer is important for effective treatment. Here, we have developed a CMBR method utilizing computational functional networks based on DNA methylation to identify conserved subgroups in HR+ breast cancer. Calculated by CMBR, HR+ breast cancer was divided into five subgroups, of which HR+/negative epidermal growth factor receptor-2 (Her2-) was divided into two subgroups, and HR+/positive epidermal growth factor receptor-2 (Her2+) was divided into three subgroups. These subgroups had heterogeneity in the immune microenvironment, tumor infiltrating lymphocyte patterns, somatic mutation patterns and drug sensitivity. Specifically, CMBR identified two subgroups with the "Hot" tumor phenotype. In addition, these conserved subgroups were broadly validated on external validation datasets. CMBR identified the molecular signature of HR+ breast cancer subgroups, providing valuable insights into personalized treatment strategies and management options.

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.

A First Computational Frame for Recognizing Heparin-Binding Protein

Heparin-binding protein (HBP) is a cationic antibacterial protein derived from multinuclear neutrophils and an important biomarker of infectious diseases. The correct identification of HBP is of great significance to the study of infectious diseases. This work provides the first HBP recognition framework based on machine learning to accurately identify HBP. By using four sequence descriptors, HBP and non-HBP samples were represented by discrete numbers. By inputting these features into a support vector machine (SVM) and random forest (RF) algorithm and comparing the prediction performances of these methods on training data and independent test data, it is found that the SVM-based classifier has the greatest potential to identify HBP. The model could produce an auROC of 0.981 ± 0.028 on training data using 10-fold cross-validation and an overall accuracy of 95.0% on independent test data. As the first model for HBP recognition, it will provide some help for infectious diseases and stimulate further research in related fields.

Recall DNA methylation levels at low coverage sites using a CNN model in WGBS

DNA methylation is an important regulator of gene transcription. WGBS is the gold-standard approach for base-pair resolution quantitative of DNA methylation. It requires high sequencing depth. Many CpG sites with insufficient coverage in the WGBS data, resulting in inaccurate DNA methylation levels of individual sites. Many state-of-arts computation methods were proposed to predict the missing value. However, many methods required either other omics datasets or other cross-sample data. And most of them only predicted the state of DNA methylation. In this study, we proposed the RcWGBS, which can impute the missing (or low coverage) values from the DNA methylation levels on the adjacent sides. Deep learning techniques were employed for the accurate prediction. The WGBS datasets of H1-hESC and GM12878 were down-sampled. The average difference between the DNA methylation level at 12× depth predicted by RcWGBS and that at >50× depth in the H1-hESC and GM2878 cells are less than 0.03 and 0.01, respectively. RcWGBS performed better than METHimpute even though the sequencing depth was as low as 12×. Our work would help to process methylation data of low sequencing depth. It is beneficial for researchers to save sequencing costs and improve data utilization through computational methods.

Prognosis Prediction of Lung Adenocarcinoma Patients Based on Molecular Subgroups of DNA Methylation

Lung adenocarcinoma (LUAD) is a malignant tumor with high mortality. At present, the clinicopathologic feature is the main breakthrough to assess the prognosis of LUAD patients. However, in most cases, the results are less than satisfactory. Cox regression analysis was conducted in this study to obtain methylation sites with significant prognostic relevance based on mRNA expression, DNA methylation data, and clinical data of LUAD from The Cancer Genome Atlas Program database. LUAD patients were grouped into 4 subtypes according to different methylation levels using K-means consensus cluster analysis. By survival analysis, patients were grouped into high-methylation and low-methylation groups. Later, 895 differentially expressed genes (DEGs) were obtained. Eight optimal methylation signature genes associated with prognosis were screened by Cox regression analysis, and a risk assessment model was constructed based on these genes. Samples were then classified into high-risk and low-risk groups depending on the risk assessment model, and prognostic, predictive ability was assessed using survival and receiver operating characteristic (ROC) curves. The results showed that this risk model had a great efficacy in predicting the prognosis of patients, and it was, therefore, able to be an independent prognostic factor. At last, the enrichment analysis demonstrated that the signaling pathways, including cell cycle, homologous recombination, P53 signaling pathway, DNA replication, pentose phosphate pathway, and glycolysis gluconeogenesis were remarkably activated in the high-risk group. In general, we construct an 8-gene model based on DNA methylation molecular subtypes by a series of bioinformatics methods, which can provide new insights for predicting the prognosis of patients with LUAD.

scWECTA: A weighted ensemble classification framework for cell type assignment based on single cell transcriptome

Rapid advances in single-cell transcriptome analysis provide deeper insights into the study of tissue heterogeneity at the cellular level. Unsupervised clustering can identify potential cell populations in single-cell RNA-sequencing (scRNA-seq) data, but fail to further determine the identity of each cell. Existing automatic annotation methods using scRNA-seq data based on machine learning mainly use single feature set and single classifier. In view of this, we propose a Weighted Ensemble classification framework for Cell Type Annotation, named scWECTA, which improves the accuracy of cell type identification. scWECTA uses five informative gene sets and integrates five classifiers based on soft weighted ensemble framework. And the ensemble weights are inferred through the constrained non-negative least squares. Validated on multiple pairs of scRNA-seq datasets, scWECTA is able to accurately annotate scRNA-seq data across platforms and across tissues, especially for imbalanced data containing rare cell types. Moreover, scWECTA outperforms other comparable methods in balancing the prediction accuracy of common cell types and the unassigned rate of non-common cell types at the same time. The source code of scWECTA is freely available at https://github.com/ttren-sc/scWECTA.

Computational prediction of promotors in Agrobacterium tumefaciens strain C58 by using the machine learning technique

Promotors are those genomic regions on the upstream of genes, which are bound by RNA polymerase for starting gene transcription. Because it is the most critical element of gene expression, the recognition of promoters is crucial to understand the regulation of gene expression. This study aimed to develop a machine learning-based model to predict promotors in Agrobacterium tumefaciens (A. tumefaciens) strain C58. In the model, promotor sequences were encoded by three different kinds of feature descriptors, namely, accumulated nucleotide frequency, k-mer nucleotide composition, and binary encodings. The obtained features were optimized by using correlation and the mRMR-based algorithm. These optimized features were inputted into a random forest (RF) classifier to discriminate promotor sequences from non-promotor sequences in A. tumefaciens strain C58. The examination of 10-fold cross-validation showed that the proposed model could yield an overall accuracy of 0.837. This model will provide help for the study of promoters in A. tumefaciens C58 strain.

Epigenetic Mechanisms in Breast Adenocarcinoma: Novel DNA Methylation Patterns

Breast adenocarcinoma is a leading cause of death in females worldwide. A broad spectrum of genetic and epigenetic alterations has been already identified and reported in millions of examined cancerous substrates, evidence of a high-level genomic heterogeneity that characterizes these malignancies. Concerning epigenetic changes and imbalances that critically affect progression and prognosis in the corresponding patients, DNA methylation, histone modifications (acetylation), micro-RNAs (miRs) alterations and chromatin re-organization represent the main mechanisms. Referring to DNA methylation, promoter hyper-hypo methylation in critical tumour suppressor and oncogenes is implicated in normal epithelia transformation to their neoplastic and finally malignant cyto-phenotypes. The current review is focused on the different methylation patterns and mechanisms detected in breast adenocarcinoma and their impact on the corresponding groups of patient response to specific chemotherapeutic regimens and life span prognosis.

Classification of Subgroups with Immune Characteristics Based on DNA Methylation in Luminal Breast Cancer

Luminal breast cancer (BC) accounts for a large proportion of patients in BC, with high heterogeneity. Determining the precise subtype and optimal selection of treatment options for luminal BC is a challenge. In this study, we proposed an MSBR framework that integrate DNA methylation profiles and transcriptomes to identify immune subgroups of luminal BC. MSBR was implemented both on a key module scoring algorithm and “Boruta” feature selection method by DNA methylation. Luminal A was divided into two subgroups and luminal B was divided into three subgroups using the MSBR. Furthermore, these subgroups were defined as different immune subgroups in luminal A and B respectively. The subgroups showed significant differences in DNA methylation levels, immune microenvironment (immune cell infiltration, immune checkpoint PD1/PD-L1 expression, immune cell cracking activity (CYT)) and pathology features (texture, eccentricity, intensity and tumor-infiltrating lymphocytes (TILs)). The results also showed that there is a subgroup in both luminal A and B that has the benefit from immunotherapy. This study proposed a classification of luminal BC from the perspective of epigenetics and immune characteristics, which provided individualized treatment decisions.

Classification and Prognosis Analysis of Pancreatic Cancer Based on DNA Methylation Profile and Clinical Information

Pancreatic adenocarcinoma (PAAD) has a poor prognosis with high individual variation in the treatment response among patients; however, there is no standard molecular typing method for PAAD prognosis in clinical practice. We analyzed DNA methylation data from The Cancer Genome Atlas database, which identified 1235 differentially methylated DNA genes between PAAD and adjacent tissue samples. Among these, 78 methylation markers independently affecting PAAD prognosis were identified after adjusting for significant clinical factors. Based on these genes, two subtypes of PAAD were identified through consistent clustering. Fourteen specifically methylated genes were further identified to be associated with survival. Further analyses of the transcriptome data identified 301 differentially expressed cancer driver genes between the two PAAD subtypes and the degree of immune cell infiltration differed significantly between the subtypes. The 14 specific genes characterizing the unique methylation patterns of the subtypes were used to construct a Bayesian network-based prognostic prediction model for typing that showed good predictive value (area under the curve value of 0.937). This study provides new insight into the heterogeneity of pancreatic tumors from an epigenetic perspective, offering new strategies and targets for personalized treatment plan evaluation and precision medicine for patients with PAAD.

Identification of cervical squamous cell carcinoma feature genes and construction of a prognostic model based on immune-related features

As heterogeneity of cervical squamous cell carcinoma (CSCC), prognosis assessment for CSCC patients remain challenging. To develop novel prognostic strategies for CSCC patients, associated biomarkers are urgently needed. This study aimed to cluster CSCC samples from a molecular perspective. CSCC expression data sets were obtained from The Cancer Genome Atlas and based on the accessed expression profile, a co-expression network was constructed with weighted gene co-expression network analysis to form different gene modules. Tumor microenvironment was evaluated using ESTIMATE algorithm, observing that the brown module was highly associated with tumor immunity. CSCC samples were clustered into three subtypes by consensus clustering based on gene expression profiles in the module. Gene set variation analysis showed differences in immune-related pathways among the three subtypes. CIBERSORT and single-sample gene set enrichment analysis analyses showed the difference in immune cell infiltration among subtype groups. Also, Human leukocyte antigen protein expression varied considerably among subtypes. Subsequently, univariate, Lasso and multivariate Cox regression analyses were performed on the genes in the brown module and an 8-gene prognostic model was constructed. Kaplan-Meier analysis illuminated that the low-risk group manifested a favorable prognosis, and receiver operating characteristic curve showed that the model has good predictive performance. qRT-PCR was used to examine the expression status of the prognosis-associated genes. In conclusion, this study identified three types of CSCC from a molecular perspective and established an effective prognostic model for CSCC, which will provide guidance for clinical subtype identification of CSCC and treatment of patients.

Distinctive gene expression patterns in pregnancy-associated breast cancer

Pregnancy-associated breast cancer (PABC) is diagnosed during pregnancy or within 1 year postpartum, but the unique aspects of its etiology and pathogenesis have not been fully elucidated. This study aimed to ascertain the molecular mechanisms of PABC to facilitate diagnosis and therapeutic development. The Limma package was used to characterize the differentially expressed genes in PABC as compared to non-pregnancy-associated breast cancer (NPABC) and normal breast tissue. A total of 871 dysregulated genes were identified in the PABC versus NPABC groups and 917 in the PABC versus normal groups, with notable differences in the expression of MAGE and CXCL family genes. The dysregulated genes between the PABC and normal groups were mainly associated with signal transduction and immune response, while Kyoto Encyclopedia of Genes and Genomes analysis revealed that the dysregulated genes were enriched in immune-related pathways, including the major histocompatibility complex (MHC) class II protein complex, the type I interferon signaling pathway, regulation of α-β T-cell proliferation, and the T-cell apoptotic process. Through protein-protein interaction network construction, CD44 and BRCA1 were identified as prominent hub genes with differential expression in PABC versus NPABC. Furthermore, a cluster with eleven hub genes was identified in PABC versus normal adjacent tissues, of which the expression of EGFR, IGF1, PTGS2, FGF1, CAV1, and PLCB1 were verified to be differentially expressed in an independent cohort of PABC patients. Notably, IGF1, PTGS2, and FGF1 were demonstrated to be significantly related to patient prognosis. Our study reveals a distinctive gene expression pattern in PABC and suggests that IGF1, PTGS2, and FGF1 might serve as biomarkers for diagnosis and prognosis of PABC.

DNA methylation changes in response to neoadjuvant chemotherapy are associated with breast cancer survival

BACKGROUND

Locally advanced breast cancer is a heterogeneous disease with respect to response to neoadjuvant chemotherapy (NACT) and survival. It is currently not possible to accurately predict who will benefit from the specific types of NACT. DNA methylation is an epigenetic mechanism known to play an important role in regulating gene expression and may serve as a biomarker for treatment response and survival. We investigated the potential role of DNA methylation as a prognostic marker for long-term survival (> 5 years) after NACT in breast cancer.

METHODS

DNA methylation profiles of pre-treatment (n = 55) and post-treatment (n = 75) biopsies from 83 women with locally advanced breast cancer were investigated using the Illumina HumanMethylation450 BeadChip. The patients received neoadjuvant treatment with epirubicin and/or paclitaxel. Linear mixed models were used to associate DNA methylation to treatment response and survival based on clinical response to NACT (partial response or stable disease) and 5-year survival, respectively. LASSO regression was performed to identify a risk score based on the statistically significant methylation sites and Kaplan-Meier curve analysis was used to estimate survival probabilities using ten years of survival follow-up data. The risk score developed in our discovery cohort was validated in an independent validation cohort consisting of paired pre-treatment and post-treatment biopsies from 85 women with locally advanced breast cancer. Patients included in the validation cohort were treated with either doxorubicin or 5-FU and mitomycin NACT.

RESULTS

DNA methylation patterns changed from before to after NACT in 5-year survivors, while no significant changes were observed in non-survivors or related to treatment response. DNA methylation changes included an overall loss of methylation at CpG islands and gain of methylation in non-CpG islands, and these changes affected genes linked to transcription factor activity, cell adhesion and immune functions. A risk score was developed based on four methylation sites which successfully predicted long-term survival in our cohort (p = 0.0034) and in an independent validation cohort (p = 0.049).

CONCLUSION

Our results demonstrate that DNA methylation patterns in breast tumors change in response to NACT. These changes in DNA methylation show potential as prognostic biomarkers for breast cancer survival.

[Significance of DNA methylation assessment in the morphological diagnosis of brain tumours]

The review is focused on a relatively new research method in oncology - DNA methylation. Starting from the methylation of individual genes, the method is gradually expanding and becoming routine for studying the global structure of DNA methylation (methylome) in tumors of various localizations. For some tumors (carcinomas of the mammary and thyroid glands), the study of the global structure of DNA methylation is just beginning, while methylation classifiers have been proposed and successfully used in the Russian Federation for brain tumours and sarcomas. This article compares the fifth edition of the WHO Classification of tumours of the Central Neurvous System and the methylation brain classifier.

Pediatric Pan-Central Nervous System Tumor Methylome Analyses Reveal Immune-Related LncRNAs

Pediatric central nervous system (CNS) tumors are the second most common cancer diagnosis among children. Long noncoding RNAs (lncRNAs) emerge as critical regulators of gene expression, and they play fundamental roles in immune regulation. However, knowledge on epigenetic changes in lncRNAs in diverse types of pediatric CNS tumors is lacking. Here, we integrated the DNA methylation profiles of 2,257 pediatric CNS tumors across 61 subtypes with lncRNA annotations and presented the epigenetically regulated landscape of lncRNAs. We revealed the prevalent lncRNA methylation heterogeneity across pediatric pan-CNS tumors. Based on lncRNA methylation profiles, we refined 14 lncRNA methylation clusters with distinct immune microenvironment patterns. Moreover, we found that lncRNA methylations were significantly correlated with immune cell infiltrations in diverse tumor subtypes. Immune-related lncRNAs were further identified by investigating their correlation with immune cell infiltrations and potentially regulated target genes. LncRNA with methylation perturbations potentially regulate the genes in immune-related pathways. We finally identified several candidate immune-related lncRNA biomarkers (i.e., SSTR5-AS1, CNTN4-AS1, and OSTM1-AS1) in pediatric cancer for further functional validation. In summary, our study represents a comprehensive repertoire of epigenetically regulated immune-related lncRNAs in pediatric pan-CNS tumors, and will facilitate the development of immunotherapeutic targets.

A Novel Five-Gene Signature Related to Clinical Outcome and Immune Microenvironment in Breast Cancer

Breast cancer (BC) is the most frequent cancer in women and the main cause of cancer-related deaths in the globe, according to the World Health Organization. The need for biomarkers that can help predict survival or guide treatment decisions in BC patients is critical in order to provide each patient with an individualized treatment plan due to the wide range of prognoses and therapeutic responses. A reliable prognostic model is essential for determining the best course of treatment for patients. Patients’ clinical and pathological data, as well as their mRNA expression levels at level 3, were gleaned from the TCGA databases. Differentially expressed genes (DEGs) between BC and non-tumor specimens were identified. Tumor immunity analyses have been utilized in order to decipher molecular pathways and their relationship to the immune system. The expressions of KIF4A in BC cells were determined by RT-PCR. To evaluate the involvement of KIF4A in BC cell proliferation, CCK-8 tests were used. In this study, utilizing FC > 4 and p < 0.05, we identified 140 upregulated genes and 513 down-regulated genes. A five-gene signature comprising SFRP1, SAA1, RBP4, KIF4A and COL11A1 was developed for the prediction of overall survivals of BC. Overall survival was distinctly worse for patients in the high-risk group than those in the low-risk group. Cancerous and aggressiveness-related pathways and decreased B cell, T cell CD4⁺, T cell CD8⁺, Neutrophil and Myeloid dendritic cells levels were seen in the high-risk group. In addition, we found that KIF4A was highly expressed in BC and its silence resulted in the suppression of the proliferation of BC cells. Taken together, as a possible prognostic factor for BC, the five-gene profile created and verified in this investigation could guide the immunotherapy selection.

A road map for happiness: The psychological factors related cell types in various parts of human body from single cell RNA-seq data analysis

Massive evidence from all sources including zoology, neurobiology and immunology has confirmed that psychological factors can raise remarkable physiological effects. Researchers have long been aware of the potential value of these effects and wanted to harness them in the development of new drugs and therapies, for which the mechanism study is a necessary prerequisite. However, most of these studies are restricted to neuroscience, or starts with blood sample and fall into the area of immunity. In this study, we choose to focus on the psychological factor of happiness, mining existing publicly available single cell RNA sequencing (scRNA-seq) data for the expression of happiness-related genes collected from various sources of literature in all types of cells in the samples, finding that the expression of these genes is not restricted within neuro-regulated cells or tissue-resident immune cells, on the opposite, cell types that are unique to tissue and organ without direct regulation from nervous system account for the majority to express the happiness-related genes. Our research is a preliminary exploration of where our body respond to our mind at cell level, and lays the foundation for more detailed mechanism research.

Prognosis of Tumor Microenvironment in Luminal B-Type Breast Cancer

Objective

Tumor microenvironment as an important element of malignancy could help predict cancer prognosis and therapeutic response; thus, a prognostic landscape map of the tumor microenvironment in luminal B breast cancers should be developed.

Methods

The GEO and TCGA databases were employed to retrieve clinical follow-up data and expression profiles of luminal B breast cancer. CIBERSORT was applied to assess the infiltration of the tumor microenvironment of 209 patients and to construct tumor microenvironment-based subtypes of luminal B breast cancer. We also conducted Cox multivariate regression analysis to select features that could be used to develop a microenvironment signature for cancer. Samples were categorized as having low and high TME scores according to the median TME score. The correlations of prognosis and TME score, expression levels of immune factors and genomic variation, and clinical features were further investigated.

Results

We found that high TME scores were correlated with poor prognosis. The current findings showed that the expressions of multiple immune-related genes, including CXCL9, CXCL10, GZMB, and PDCD1LG2, were upregulated in cancer with high TME scores. The high-risk group showed lower TP53 gene mutation frequency as opposed to that of the low-risk group. For the purpose of developing a TME scoring system, the TME infiltration levels of 209 patients with luminal B breast cancer from TCGA were comprehensively analyzed.

Conclusions

Our analysis revealed that the TME score was an indicator of patients' response to immune checkpoint modulators and an effective prognostic biomarker. TME scoring improves current immunotherapy on luminal B breast cancer.

DNA Methylation-Driven Genes for Developing Survival Nomogram for Low-Grade Glioma

Low-grade gliomas (LGG) are heterogeneous, and the current predictive models for LGG are either unsatisfactory or not user-friendly. The objective of this study was to establish a nomogram based on methylation-driven genes, combined with clinicopathological parameters for predicting prognosis in LGG. Differential expression, methylation correlation, and survival analysis were performed in 516 LGG patients using RNA and methylation sequencing data, with accompanying clinicopathological parameters from The Cancer Genome Atlas. LASSO regression was further applied to select optimal prognosis-related genes. The final prognostic nomogram was implemented together with prognostic clinicopathological parameters. The predictive efficiency of the nomogram was internally validated in training and testing groups, and externally validated in the Chinese Glioma Genome Atlas database. Three DNA methylation-driven genes, ARL9, CMYA5, and STEAP3, were identified as independent prognostic factors. Together with IDH1 mutation status, age, and sex, the final prognostic nomogram achieved the highest AUC value of 0.930, and demonstrated stable consistency in both internal and external validations. The prognostic nomogram could predict personal survival probabilities for patients with LGG, and serve as a user-friendly tool for prognostic evaluation, optimizing therapeutic regimes, and managing LGG patients.

Landscape Analysis of Matrix Metalloproteinases Unveils Key Prognostic Markers for Patients With Breast Cancer

Breast cancer (BRCA) is the most common cancer in the world, of which incidence rate and mortality are the highest in women. Being responsible for the remodeling and degradation of extracellular matrix proteins, matrix metalloproteinases (MMPs) have been regarded as one of the most important protease family related to tumorigenesis. It has been demonstrated that MMPs play crucial roles in some tumor invasion and metastasis. However, the potential roles of MMPs in tumorigenesis and progression of BRCA and its subtype remain elusive. Herein, we conducted a systematic study on MMPs via a series of database-based retrospective analysis, including TCGA, R Studio, GEPIA, Kaplan-Meier Plotter, cBioPortal, STRING, GeneMANIA and TIMER. As a result, many MMP family members were differentially expressed in patients with BRCA, e.g., the expressions of MMP1, MMP9, MMP11 and MMP13 were up-regulated, whereas the expression levels of MMP19 and MMP28 were down-regulated. MMP9, MMP12, MMP15 and MMP27 were significantly correlated with the clinical stages of BRCA, implying their important roles in the occurrence and development of BRCA. In addition, the survival analysis indicated that different expression pattern of MMPs exhibited distinct outcomes in patient with BRCA, e.g., patients with high expression of MMP2, MMP8, MMP16, MMP17, MMP19, MMP20, MMP21, MMP24, MMP25, MMP26 and MMP27 had a prolonged survival time, while the others (MMP1, MMP7, MMP9, MMP12 and MMP15) exhibited poor prognosis. Subsequent functional and network analysis revealed MMPs were mainly correlated with parathyroid hormone synthesis and secretion pathway, collagen metabolism, and their effect on the activities of serine hydrolase, serine peptidase and aminopeptidase. Notably, our analysis showed that the expression of MMPs was significantly correlated with the infiltration of various immune cells in BRCA, including CD8+T cells, CD4+T cells, macrophages, neutrophils, B cells, and dendritic cells, suggesting the close correlations between MMPs and immune functions. In short, our study disclosed MMPs play multiple biological roles in the development of BRCA, MMP1 and MMP9 might be used as independent prognostic markers and potential therapeutic targets for diagnosis and treatment for patients with BRCA.

Screening and Comprehensive Analysis of Cancer-Associated tRNA-Derived Fragments

tRNA-derived fragments (tRFs) constitute a novel class of small non-coding RNA cleaved from tRNAs. In recent years, researches have shown the regulatory roles of a few tRFs in cancers, illuminating a new direction for tRF-centric cancer researches. Nonetheless, more specific screening of tRFs related to oncogenesis pathways, cancer progression stages and cancer prognosis is continuously demanded to reveal the landscape of the cancer-associated tRFs. In this work, by combining the clinical information recorded in The Cancer Genome Atlas (TCGA) and the tRF expression profiles curated by MINTbase v2.0, we systematically screened 1,516 cancer-associated tRFs (ca-tRFs) across seven cancer types. The ca-tRF set collectively combined the differentially expressed tRFs between cancer samples and control samples, the tRFs significantly correlated with tumor stage and the tRFs significantly correlated with patient survival. By incorporating our previous tRF-target dataset, we found the ca-tRFs tend to target cancer-associated genes and onco-pathways like ATF6-mediated unfolded protein response, angiogenesis, cell cycle process regulation, focal adhesion, PI3K-Akt signaling pathway, cellular senescence and FoxO signaling pathway across multiple cancer types. And cell composition analysis implies that the expressions of ca-tRFs are more likely to be correlated with T-cell infiltration. We also found the ca-tRF expression pattern is informative to prognosis, suggesting plausible tRF-based cancer subtypes. Together, our systematic analysis demonstrates the potentially extensive involvements of tRFs in cancers, and provides a reasonable list of cancer-associated tRFs for further investigations.

Regulatory Mechanism of Neurotrophin Receptor-Interacting Melanoma Antigen Coding Gene Homolog (NRAGE) Gene Methylation on Apoptosis of Breast Cancer Cell Under Tyrosine Kinases/Methyl Ethyl Ketone/Extracellular Regulated Protein Kinases Signaling Pathway

The aim of this study was to discover the influence of Neurotrophin receptor-interacting MAGE homolog (NRAGE) gene methylation on proliferation (Pro) and apoptosis (Apo) of breast cancer cell (BCC), and its influence on TrkA/MEK/ERK signaling. BCC lines MCF-7, MDA-MB-231, and normal mammary gland cell (MGC) MCF-10 were selected. Expression of NRAGE mRNA and methylation level in cells was analyzed via reverse transcription-polymerase chain reaction (RT-PCR) and methylation-specific PCR. Different concentrations (0, 5, 10 mol/L) of DNA methylase inhibitor 5-aza-2′-deoxycytidine (5-Aza-CdR) were adopted to treat the BCC cell line. With dimethyl sulfoxide (DMSO) treatment as control, cell count, 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay, flow cytometry, and Western blot were adopted to detect the Pro, Apo, relative expression (REP) of Apo-related proteins Bcl-2, Bax, and target proteins TrkA, MEK, and ERK1/2 after different treatments. The results showed that NRAGE mRNA level in MDA-MB-231 and MCF-7 was notably reduced versus MCF-10 (P < 0.05), and they could express methylated NRAGE specifically. 5-Aza-CdR can increase unmethylated NRAGE’s expression in BCC. Cell Pro level of the 5 and 10 mol/L treatments was greatly inhibited than DMSO and 0 mol/L treatments (P < 0.05). Apo rate and Apo-related proteins Bcl-2 and Bax increased obviously (P < 0.05). In addition, the phosphorylation levels of TrkA in the 5 and 10 mol/L treatments were considerably reduced (P < 0.05), while that in MEK and ERK1/2 was remarkably increased (P < 0.05). In short, NRAGE methylation can inhibit BCC’s Pro and regulate BCC’s Pro and Apo through TrkA/MEK/ERK signaling.

Application of Sparse Representation in Bioinformatics

Inspired by L1-norm minimization methods, such as basis pursuit, compressed sensing, and Lasso feature selection, in recent years, sparse representation shows up as a novel and potent data processing method and displays powerful superiority. Researchers have not only extended the sparse representation of a signal to image presentation, but also applied the sparsity of vectors to that of matrices. Moreover, sparse representation has been applied to pattern recognition with good results. Because of its multiple advantages, such as insensitivity to noise, strong robustness, less sensitivity to selected features, and no “overfitting” phenomenon, the application of sparse representation in bioinformatics should be studied further. This article reviews the development of sparse representation, and explains its applications in bioinformatics, namely the use of low-rank representation matrices to identify and study cancer molecules, low-rank sparse representations to analyze and process gene expression profiles, and an introduction to related cancers and gene expression profile database.

Integrated analysis of hypoxia-associated lncRNA signature to predict prognosis and immune microenvironment of lung adenocarcinoma patients

Lung adenocarcinoma (LUAD) represents the main lung cancer (LC) subtype that possesses a disappointing clinical outcome over the decades. Tumor hypoxia is closely bound up with dismal survival for malignant tumor cases. We identified hypoxia-associated long non-coding RNA (lncRNA) signature to be an explicit indicator for predicting prognosis. The present work acquired RNA-seq and associated clinical data from The Cancer Genome Atlas (TCGA) database. Consensus cluster analysis characterized the hypoxia status of LUAD patients. Cox regression analysis with the least absolute shrinkage and selection operator (LASSO) method determined significantly prognosis-related lncRNAs which were used to create a prognostic model. Diverse statistical approaches like the Kaplan-Meier curve, receiver operating characteristic (ROC) curve, and nomogram were adopted to verify the accuracy of the risk score. The potential immune environment landscape was unearthed by the CIBERSORT algorithm. Three hypoxia-related clusters were determined and 221 differentially expressed hypoxia-related lncRNAs were screened out. We developed a new predictive model based on seven lncRNAs (LINC00941, AC022784.1, AC079949.2, LINC00707, AL161431.1, AC010980.2 and AC090001.1). Kaplan-Meier curves and ROC plots uncovered the reliable predictive power of the risk score model. In addition, the immunosuppressive landscape was presented in the high-risk group by immune cell infiltration analysis. The seven hypoxia lncRNAs survival signature in our article are robust, accurate tools for predicting overall survival in LUAD patients.

N6-Methyladenosine Regulators Are Involved in the Progression of and Have Clinical Impact on Breast Cancer

Background

N6-methyladenosine (m⁶A) modification has been widely studied in various cancers, and m⁶A regulators, such as METTL3, METTL14, WTAP, and YTHDF1, play crucial roles in breast cancer. However, a comprehensive study of m⁶A regulators in breast cancer is still lacking.

Material/Methods

Expression data of m⁶A regulators and clinicopathological information were acquired from The Cancer Genome Atlas (TCGA) program. Protein interaction was collected from the STRING database. Data on tumor purity and correlation among m⁶A regulators were obtained from the TIMER database. LASSO, consensus clustering, and gene set enrichment analysis (GSEA) were used to evaluate the role of m⁶A regulators. Moreover, the prognostic value of m⁶A-related genomic targets in breast cancer was analyzed by Kaplan-Meier analysis and Cox regression models.

Results

We found most m⁶A regulators were associated with key clinicopathological parameters, such as tumor staging, Nottingham prognostic index (NPI), and cellularity. Also, consensus clustering analysis-based grouping could effectively predict patients’ overall survival. Correlation analysis also showed that these regulators interacted with each other. Patients were further split into a high-risk group and low-risk group based on Cox and LASSO analysis. High-risk patients had a significantly worse overall survival than did low-risk patients. Moreover, AKT1 and MYC were enriched in patients in the high-risk group, according to GSEA analysis. The patients in the high-risk group also displayed resistance to chemoradiotherapy or hormone therapy.

Conclusions

The m⁶A regulators are critical participants in the development and progression of breast cancer and are likely to be used to predict prognosis and develop treatment strategies.

Identification of inflammation-related DNA methylation biomarkers in periodontitis patients based on weighted co-expression analysis

Evidence from past research has shown that DNA methylation plays a key role in the pathogenesis of periodontitis, regulating gene expression levels and thereby affecting the occurrence of various diseases. Three sample sets of methylation data and gene expression data were downloaded from Gene Expression Omnibus (GEO) database. A diagnostic classifier is established based on gene expression data and CpG methylation data. Abnormal expression of immune-related pathways and methyltransferase-related genes in patients with periodontitis was detected. A total of 8,029 differentially expressed CpG (DMP) was annotated to the promoter region of 4,940 genes, of which 295 immune genes were significantly enriched. The CpG sites of 23 differentially co-expressed immune gene promoter regions were identified, and 13 CpG were generally hypermethylated in healthy group samples, while some were methylated in most patients. Five CpGs were screened as robust periodontitis biomarkers. The accuracy in the training data set, the two external verification data sets, and in the transcriptome was 95.5%, 80% and 78.3%, and 82.6%, respectively. This study provided new features for the diagnosis of periodontitis, and contributed to the personalized treatment of periodontitis.

refMiR 142 5p inhibits cell invasion and migration by targeting DNMT1 in breast cancer

Abnormal cell proliferation caused by abnormal transcription regulation mechanismseems to be one of the reasons for the progression of breast cancer and also thepathological basis. MicroRNA 142 5p (miR 142 5p) is a low expressed miRNA inbreast cancer. T he role of MKL1's regulation of DNMT1 in breast cancer cellproliferation and migration is still unclear. MKL 1 (myocardi n related transcriptionfactor A) can bind to the conserved cis regulatory element CC (A/T) 6GG (called CarGbox) in the promoter to re gulate the transcription of miR 142 5p. The expression ofmiR 142 5p and MKL 1 are positively correlated. In addition, it has been proved thatDNMT1 is the target of miR 142 5p, which inhibits the expression of DNMT1 bytargeting the 3'UTR of DNMT1, thereby forming a feedback loop and inhibiting themigration and proliferation of breast cancer. Our data provide important and novelinsights into the MKL 1/miR 142 5p/DNMT1/maspin signaling pathway, and maybecome a new idea for breast cancer diagnosis, treatment and prognosis.

A High Epigenetic Risk Score Shapes the Non-Inflamed Tumor Microenvironment in Breast Cancer

Background: Epigenetic dysregulation via aberrant DNA methylation has gradually become recognized as an efficacious signature for predicting tumor prognosis and response to therapeutic targets. However, reliable DNA methylation biomarkers describing tumorigenesis remain to be comprehensively explored regarding their prognostic and therapeutic potential in breast cancer (BC).

Methods: Whole-genome methylation datasets integrated from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database were profiled (n = 1,268). A three-stage selection procedure (discovery, training, and external validation) was utilized to screen out the prominent biomarkers and establish a robust risk score from more than 300,000 CpG sites after quality control, rigorous filtering, and reducing dimension. Moreover, gene set enrichment analyses guided us to systematically correlate this epigenetic risk score with immunological characteristics, including immunomodulators, anti-cancer immunity cycle, immune checkpoints, tumor-infiltrating immune cells and a series of signatures upon modulating components within BC tumor microenvironment (TME). Multi-omics data analyses were performed to decipher specific genomic alterations in low- and high-risk patients. Additionally, we also analyzed the role of risk score in predicting response to several treatment options.

Results: A 10-CpG-based prognostic signature which could significantly and independently categorize BC patients into distinct prognoses was established and sufficiently validated. And we hypothesize that this signature designs a non-inflamed TME in BC based on the evidence that the derived risk score is negatively correlated with tumor-associated infiltrating immune cells, anti-cancer immunity cycle, immune checkpoints, immune cytolytic activity, T cell inflamed score, immunophenoscore, and the vast majority of immunomodulators. The identified high-risk patients were characterized by upregulation of immune inhibited oncogenic pathways, higher TP53 mutation and copy number burden, but lower response to cancer immunotherapy and chemotherapy.

Conclusion: Our work highlights the complementary roles of 10-CpG-based signature in estimating overall survival in BC patients, shedding new light on investigating failed events concerning immunotherapy at present.

Integrated Analyses Reveal the Multi-Omics and Prognostic Characteristics of ATP5B in Breast Cancer

The beta subunit of F1Fo-ATP synthase (ATP5B) has been demonstrated to play an essential role in tumor progression and metastasis. However, there has been no comprehensive pan-cancer multi-omics analysis of ATP5B, while the clinical relevance of ATP5B and its potential mechanism in regulating breast cancer are still poorly understood. In this study, we demonstrated that ATP5B has a higher frequency of amplification than deletion in most cancer types, and the copy number variation (CNV) of ATP5B was significantly positively correlated with its mRNA expression level. DNA methylation analysis across pan-cancer also revealed a strong correlation between ATP5B expression and epigenetic changes. We identified 6 significant methylation sites involved in the regulation of ATP5B expression. Tissue microarrays (TMA) from 129 breast cancer samples, integrated with multiple additional breast cancer dataset, were used to evaluate the ATP5B expression and its correlation with prognosis. Higher levels of ATP5B expression were consistently associated with a worse OS in all datasets, and Cox regression analysis suggested that ATP5B expression was an independent prognostic factor. Gene enrichment analysis indicated that the gene signatures of DNA damage recognition, the E-cadherin nascent pathway and the PLK1 pathway were enriched in ATP5B-high patients. Moreover, somatic mutation analysis showed that a significant different mutation frequency of CDH1 and ADAMTSL3 could be observed between the ATP5B-high and ATP5B-low groups. In conclusion, this study reveals novel significance regarding the genetic characteristics and clinical value of ATP5B highlighted in predicting the outcome of breast cancer patients.

Comprehensive Bioinformatics Analysis Identifies Tumor Microenvironment and Immune-related Genes in Small Cell Lung Cancer

BACKGROUND

Tumor microenvironment (TME) cells play important roles in tumor progression. Accumulating evidence show that they can be exploited to predict the clinical outcomes and therapeutic responses of the tumor. However, the role of immune genes of TME in small cell lung cancer (SCLC) is currently unknown.

OBJECTIVE

To determine the role of immune genes in SCLC.

METHODS

We downloaded the expression profile and clinical follow-up data of SCLC patients from Gene Expression Omnibus (GEO), and TME infiltration profile data of 158 patients using CIBERSORT. The correlation between TME phenotypes, genomic features, and clinicopathological features of SCLC was examined. A gene signature was constructed based on TME genes to further evaluate the relationship between molecular subtypes of SCLC with the prognosis and clinical features.

RESULTS

We identified a group of genes that are highly associated with TME. Several immune cells in TME cells were significantly correlated with SCLC prognosis (p<0.0001). These immune cells displayed diverse immune patterns. Three molecular subtypes of SCLC (TMEC1-3) were identified on the basis of enrichment of immune cell components, and these subtypes showed dissimilar prognosis profiles (p=0.03). The subtype with the best prognosis, TMEC3, was enriched with immune activation factors such as oncogene M0, oncogene M2, T cells follicular helper, and T cells CD8 (p<0.001). The TMEC1 subtype with the worst prognosis was enriched with T cells CD4 naive, B cells memory and Dendritic cells activated cells (p<0.001). Further analysis showed that the TME was significantly enriched with immune checkpoint genes, immune genes, and immune pathway genes (p<0.01). From the gene expression data, we identified four TME-related genes, GZMB, HAVCR2, PRF1 and TBX2, which were significantly associated with poor prognosis in both the training set and the validation set (p<0.05). These genes may serve as markers for monitoring tumor responses to immune checkpoint inhibitors.

CONCLUSION

This study shows that TME features may serve as markers for evaluating the response of SCLC cells to immunotherapy.

NEFM DNA methylation correlates with immune infiltration and survival in breast cancer

Background

This study aims to determine whether NEFM (neurofilament medium) DNA methylation correlates with immune infiltration and prognosis in breast cancer (BRCA) and to explore NEFM-connected immune gene signature.

Methods

NEFM transcriptional expression was analyzed in BRCA and normal breast tissues using Oncomine and Tumor Immune Estimation Resource (TIMER) databases. The relationship between NEFM DNA methylation and NEFM transcriptional expression was investigated in TCGA. Potential influence of NEFM DNA methylation/expression on clinical outcome was evaluated using TCGA BRCA, The Human Protein Atlas and Kaplan–Meier plotter databases. Association of NEFM transcriptional expression/DNA methylation with cancer immune infiltration was investigated using TIMER and TISIDB databases.

Results

High expression of NEFM correlated with better overall survival (OS) and recurrence-free survival (RFS) in TCGA BRCA and Kaplan–Meier plotter, whereas NEFM DNA methylation with worse OS in TCGA BRCA. NEFM transcriptional expression negatively correlated with DNA methylation. NEFM DNA methylation significantly negatively correlated with infiltrating levels of B, CD8⁺ T/CD4⁺ T cells, macrophages, neutrophils and dendritic cells in TIMER and TISIDB. NEFM expression positively correlated with macrophage infiltration in TIMER and TISIDB. After adjusted with tumor purity, NEFM expression weekly negatively correlated with infiltration level of B cells, whereas positively correlated with CD8⁺ T cell infiltration in TIMER gene modules. NEFM expression/DNA methylation correlated with diverse immune markers in TCGA and TISIDB.

Conclusions

NEFM low-expression/DNA methylation correlates with poor prognosis. NEFM expression positively correlates with macrophage infiltration. NEFM DNA methylation strongly negatively correlates with immune infiltration in BRCA. Our study highlights novel potential functions of NEFM expression/DNA methylation in regulation of tumor immune microenvironment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-021-01096-4.

Identification of an Immune Gene Signature Based on Tumor Microenvironment Characteristics in Colon Adenocarcinoma

Tumor microenvironment (TME) changes are related to the occurrence and development of colon adenocarcinoma (COAD). This study aimed to analyze the characteristics of the immune microenvironment in CC, as well as the microenvironment's relationship with the clinical features of CC. Based on The Cancer Genome Atlas (TCGA) and GSE39582 cohorts, the scores of 22 tumor infiltrating lymphocytes (TILs) were calculated using CIBERSORT. ConsensusClusterPlus was used for unsupervised clustering. Three TME subtypes (TMEC1, TMEC2, and TME3) were identified based on TIL scores. TMEC2 was associated with the worst prognosis. Random forest, k-means clustering, and principal component analysis were used to construct the TME score risk signature. The median TME score was used to divide the samples into high- and low-risk groups. The prognoses of the patients with high TME scores were worse than those of the patients with low TME scores. A high TME score was an independent prognostic risk factor for patients with colon cancer. The Gene Set Enrichment Analysis (GSEA) results showed that those with high TME scores were enriched in FOCAL_ADHESION, ECM_RECEPTOR_INTERACTION, and PATHWAYS_IN_CANCER. Our findings will provide a new strategy for immunotherapy in patients with CC.

Prognostic impact of the glypican family of heparan sulfate proteoglycans on the survival of breast cancer patients

Purpose

Dysregulated expression of proteoglycans influences the outcome and progression of numerous cancers. Several studies have investigated the role of individual glypicans in cancer, however, the impact of the whole glypican family of heparan sulfate proteoglycans on prognosis of a large patient cohort of breast cancer patients has not yet been investigated. In the present study, our aim was to investigate the prognostic power of the glypicans in breast cancer patients.

Methods

We used a public database including both gene expression data and survival information for 3951 breast cancer patients to determine the prognostic value of glypicans on relapse-free survival using Cox regression analysis. Moreover, we performed quantitative Real-Time PCR to determine glypican gene expression levels in seven representative breast cancer cell lines.

Results

We found that high GPC3 levels were associated with a better prognosis in overall breast cancer patients. When stratified by hormone receptor status, we found that in worse prognosis subtypes low GPC1 levels correlate with a longer relapse-free survival, and in more favorable subtypes low GPC6 was associated with longer survival.

Conclusion

Our study concludes that glypicans could act as subtype-specific biomarkers for the prognosis of breast cancer patients and sparks hope for future research on glypicans possibly eventually providing targets for the treatment of the disease.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00432-021-03597-4.

Automatic Detection of the Circulating Cell-Free Methylated DNA Pattern of GCM2, ITPRIPL1 and CCDC181 for Detection of Early Breast Cancer and Surgical Treatment Response

The early detection of cancer can reduce cancer-related mortality. There is no clinically useful noninvasive biomarker for early detection of breast cancer. The aim of this study was to develop accurate and precise early detection biomarkers and a dynamic monitoring system following treatment. We analyzed a genome-wide methylation array in Taiwanese and The Cancer Genome Atlas (TCGA) breast cancer (BC) patients. Most breast cancer-specific circulating methylated CCDC181, GCM2 and ITPRIPL1 biomarkers were found in the plasma. An automatic analysis process of methylated ccfDNA was established. A combined analysis of CCDC181, GCM2 and ITPRIPL1 (CGIm) was performed in R using Recursive Partitioning and Regression Trees to establish a new prediction model. Combined analysis of CCDC181, GCM2 and ITPRIPL1 (CGIm) was found to have a sensitivity level of 97% and an area under the curve (AUC) of 0.955 in the training set, and a sensitivity level of 100% and an AUC of 0.961 in the test set. The circulating methylated CCDC181, GCM2 and ITPRIPL1 was also significantly decreased after surgery (all p < 0.001). The aberrant methylation patterns of the CCDC181, GCM2 and ITPRIPL1 genes means that they are potential biomarkers for the detection of early BC and can be combined with breast imaging data to achieve higher accuracy, sensitivity and specificity, facilitating breast cancer detection. They may also be applied to monitor the surgical treatment response.

A novel DNA methylation-based model that effectively predicts prognosis in hepatocellular carcinoma

PURPOSE

To build a novel predictive model for hepatocellular carcinoma (HCC) patients based on DNA methylation data.

METHODS

Four independent DNA methylation datasets for HCC were used to screen for common differentially methylated genes (CDMGs). Gene Ontology (GO) enrichment, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were used to explore the biological roles of CDMGs in HCC. Univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) Cox analysis were performed to identify survival-related CDMGs (SR-CDMGs) and to build a predictive model. The importance of this model was assessed using Cox regression analysis, propensity score-matched (PSM) analysis and stratification analysis. A validation group from the Cancer Genome Atlas (TCGA) was constructed to further validate the model.

RESULTS

Four SR-CDMGs were identified and used to build the predictive model. The risk score of this model was calculated as follows: risk score = (0.01489826 × methylation level of WDR69) + (0.15868618 × methylation level of HOXB4) + (0.16674959 × methylation level of CDKL2) + (0.16689301 × methylation level of HOXA10). Kaplan-Meier analysis demonstrated that patients in the low-risk group had a significantly longer overall survival (OS; log-rank P-value =0.00071). The Cox model multivariate analysis and PSM analysis identified the risk score as an independent prognostic factor (P<0.05). Stratified analysis results further confirmed this model performed well. By analyzing the validation group, the results of receiver operating characteristic (ROC) curve analysis and survival analysis further validated this model.

CONCLUSION

Our DNA methylation-based prognosis predictive model is effective and reliable in predicting prognosis for patients with HCC.

Reclassification of Kidney Clear Cell Carcinoma Based on Immune Cell Gene-Related DNA CpG Pairs

Background: A new method was developed based on the relative ranking of gene expression level, overcoming the flaw of the batch effect, and having reliable results in various studies. In the current study, we defined the two methylation sites as a pair. The methylation level in a specific sample was subject to pairwise comparison to calculate a score for each CpGs-pair. The score was defined as a CpGs-pair score. If the first immune-related CpG value was higher than the second one in a specific CpGs-pair, the output score of this immune-related CpGs-pair was 1; otherwise, the output score was 0. This study aimed to construct a new classification of Kidney Clear Cell Carcinoma (KIRC) based on DNA CpGs (methylation sites) pairs. Methods: In this study, the biomarkers of 28 kinds of immune infiltration cells and corresponding methylation sites were acquired. The methylation data were compared between KIRC and normal tissue samples, and differentially methylated sites (DMSs) were obtained. Then, DNA CpGs-pairs were obtained according to the pairs of DMSs. In total, 441 DNA CpGs-pairs were utilized to construct a classification using unsupervised clustering analysis. We also analyzed the potential mechanism and therapy of different subtypes, and validated them in a testing set. Results: The classification of KIRC contained three subgroups. The clinicopathological features were different across three subgroups. The distribution of immune cells, immune checkpoints and immune-related mechanisms were significantly different across the three clusters. The mutation and copy number variation (CNV) were also different. The clinicopathological features and potential mechanism in the testing dataset were consistent with those in the training set. Conclusions: Our findings provide a new accurate and stable classification for developing personalized treatments for the new specific subtypes.

DNA methylation subtypes for ovarian cancer prognosis

Ovarian cancer is one of three major malignancies of the female reproductive system. DNA methylation (MET) is closely related to ovarian cancer occurrence and development, and as such, elucidation of effective MET subtype markers may guide individualized treatment and improve ovarian cancer prognosis. To identify potential markers, we downloaded a total of 571 ovarian cancer MET samples from The Cancer Genome Atlas (TCGA), and established a Cox proportional hazards model using the MET spectrum and clinical pathological parameters. A total of 250 prognosis-related MET loci were obtained by Cox regression, and six molecular subtypes were screened by consensus clustering of CpG loci with a significant difference in both univariate and multivariate analyses. There was a remarkable MET difference between most subtypes. Cluster 2 had the highest MET level and demonstrated the best prognosis, while Clusters 4 and 5 had MET levels significantly lower than those of the other subtypes and demonstrated very poor prognosis. All Cluster 5 samples were at a high grade, while the percentage of stage IV samples in Cluster 4 was greater than in the other subtypes. We obtained five CpG loci using a coexpression network: cg27625732, cg00431050, cg22197830, cg03152385, and cg22809047. Our cluster analysis showed that prognosis in patients with hypomethylation was significantly worse than in patients with hypermethylation. These MET molecular subtypes can be used not only to evaluate ovarian cancer prognosis, but also to fully distinguish the tumor stage and histological grade in patients with ovarian cancer.

Identification of differentially methylated genes as diagnostic and prognostic biomarkers of breast cancer

Background

Aberrant DNA methylation is significantly associated with breast cancer.

Methods

In this study, we aimed to determine novel methylation biomarkers using a bioinformatics analysis approach that could have clinical value for breast cancer diagnosis and prognosis. Firstly, differentially methylated DNA patterns were detected in breast cancer samples by comparing publicly available datasets (GSE72245 and GSE88883). Methylation levels in 7 selected methylation biomarkers were also estimated using the online tool UALCAN. Next, we evaluated the diagnostic value of these selected biomarkers in two independent cohorts, as well as in two mixed cohorts, through ROC curve analysis. Finally, prognostic value of the selected methylation biomarkers was evaluated breast cancer by the Kaplan-Meier plot analysis.

Results

In this study, a total of 23 significant differentially methylated sites, corresponding to 9 different genes, were identified in breast cancer datasets. Among the 9 identified genes, ADCY4, CPXM1, DNM3, GNG4, MAST1, mir129-2, PRDM14, and ZNF177 were hypermethylated. Importantly, individual value of each selected methylation gene was greater than 0.9, whereas predictive value for all genes combined was 0.9998. We also found the AUC for the combined signature of 7 genes (ADCY4, CPXM1, DNM3, GNG4, MAST1, PRDM14, ZNF177) was 0.9998 [95% CI 0.9994–1], and the AUC for the combined signature of 3 genes (MAST1, PRDM14, and ZNF177) was 0.9991 [95% CI 0.9976–1]. Results from additional validation analyses showed that MAST1, PRDM14, and ZNF177 had high sensitivity, specificity, and accuracy for breast cancer diagnosis. Lastly, patient survival analysis revealed that high expression of ADCY4, CPXM1, DNM3, PRDM14, PRKCB, and ZNF177 were significantly associated with better overall survival.

Conclusions

Methylation pattern of MAST1, PRDM14, and ZNF177 may represent new diagnostic biomarkers for breast cancer, while methylation of ADCY4, CPXM1, DNM3, PRDM14, PRKCB, and ZNF177 may hold prognostic potential for breast cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12957-021-02124-6.

A Methylation-Based Reclassification of Bladder Cancer Based on Immune Cell Genes

Simple Summary

Bladder cancer (BC) development is highly related to immune cell infiltration. In this study, we aimed to construct a new classification of bladder cancer molecular subtypes based on immune-cell-associated CpG(Methylation) sites. The classification was accurate and stable. BC patients were successfully divided into three subtypes based on the immune-cell-associated CpG sites. The clinicopathologic features, distribution of immune cells, level of expression of checkpoints, stromal score, immune score, ESTIMATEScore, tumor purity, APC co_inhibition, APC co_stimulation, HLA, MHC class_I, Type I IFN_respons, Type II IFN response, and DNA stemness score (DNAss) presented significant differences among the three subgroups. The specific genomic alteration was also different across subgroups. High-level immune infiltration showed a correlation with high-level methylation. A lower RNA stemness score (RNAss) was associated with higher immune infiltration. Cluster 2 demonstrated a better response to chemotherapy. The anti-cancer targeted drug therapy results are different among the three subgroups.

Abstract

Background: Bladder cancer is highly related to immune cell infiltration. This study aimed to develop a new classification of BC molecular subtypes based on immune-cell-associated CpG sites. Methods: The genes of 28 types of immune cells were obtained from previous studies. Then, methylation sites corresponding to immune-cell-associated genes were acquired. Differentially methylated sites (DMSs) were identified between normal samples and bladder cancer samples. Unsupervised clustering analysis of differentially methylated sites was performed to divide the sites into several subtypes. Then, the potential mechanism of different subtypes was explored. Results: Bladder cancer patients were divided into three groups. The cluster 3 subtype had the best prognosis. Cluster 1 had the poorest prognosis. The distribution of immune cells, level of expression of checkpoints, stromal score, immune score, ESTIMATEScore, tumor purity, APC co_inhibition, APC co_stimulation, HLA, MHC class_I, Type I IFN Response, Type II IFN Response, and DNAss presented significant differences among the three subgroups. The distribution of genomic alterations was also different. Conclusions: The proposed classification was accurate and stable. BC patients could be divided into three subtypes based on the immune-cell-associated CpG sites. Specific biological signaling pathways, immune mechanisms, and genomic alterations were varied among the three subgroups. High-level immune infiltration was correlated with high-level methylation. The lower RNAss was associated with higher immune infiltration. The study of the intratumoral immune microenvironment may provide a new perspective for BC therapy.

Identification of prognostic and immune-related gene signatures in the tumor microenvironment of endometrial cancer

Uterine corpus endometrial cancer (UCEC) is one of the most prevalent female malignancies in clinical practice. Due to the lack of effective biomarkers and personalized treatments, the prognosis of advanced-stage EC remains unfavorable. Modulation of the immune microenvironment is closely related to the onset and development of endometrial cancer. In the present study, we attempt to systematically analyze the characteristics of the immune microenvironment of endometrial cancer and investigate its association with clinical features by applying bioinformatics. RNA-Seq in TCGA (The Cancer Genome Atlas) and clinical follow-up information of patents were used for analysis. The Tumor Microenvironment (TME) score infiltration patterns of 523 endometrial cancer patients were evaluated using CIBERSORT. Random forest, multivariable cox analysis were used to build the TME score. Fisher's exact test was used to compare the genes that show significant differences in the frequency of mutations between groups. Two TME phenotypes were defined. There is a significant relationship between the TME score and grade. High TME score samples are highly expressed in immune activation, TGF pathway activation and immune checkpoint genes, and low TME score samples have high frequency mutations of PTEN, CSE1L and ITGB3. Therefore, describing the comprehensive landscape of UCEC's TME characteristics may help explain patients' response to immunotherapy and provide new strategies for cancer treatment.

Prognostic Value of DNA Methylation-Driven Genes in Clear Cell Renal Cell Carcinoma: A Study Based on Methylation and Transcriptome Analyses

Background

Few previous studies have comprehensively explored the level of DNA methylation and gene expression in ccRCC. The purpose of this study was to identify the key clear cell renal cell carcinoma- (ccRCC-) related DNA methylation-driven genes (MDG) and to build a prognostic model based on the level of DNA methylation.

Methods

RNA-seq transcriptome data and DNA methylation data were obtained from The Cancer Genome Atlas. Based on the MethylMix algorithm, we obtain ccRCC-related MDG. The univariate and multivariate Cox regression analyses were employed to investigate the correlation between patient overall survival and the methylation level of each MDG. Finally, a prognosis risk score was established based on a linear combination of the regression coefficient derived from the multivariate Cox regression model (β) multiplied with the methylation level of the gene.

Results

19 ccRCC-related MDG were identified. Three MDG (NCKAP1L, EVI2A, and BATF) were further screened and integrated into a prognostic risk score model, risk score = (3.710∗methylation level of NCKAP1L) + (-3.892∗methylation level of EVI2A) + (-3.907∗methylation level of BATF). The risk model was independent from conventional clinical characteristics as a prognostic factor for ccRCC (HR = 1.221, 95% confidence interval: 1.063-1.402, and P = 0.005). The joint survival analysis showed that the gene expression and methylation levels of the prognostic genes EVI2A and BATF were significantly related with prognosis.

Conclusion

This study provided an important bioinformatics foundation for in-depth studies of ccRCC DNA methylation.

Tumor microenvironment characterization in head and neck cancer identifies prognostic and immunotherapeutically relevant gene signatures

The tumor microenvironment (TME) is of great clinical significance for predicting the therapeutic effect of tumors. Nonetheless, there was no systematic analysis of cellular interactions in the TME of head and neck cancer (HNSC). This study used gene expression data from 816 patients with HNSC to analyze the scores of 22 immune cells. On this basis, we have established a novel TMEscore-based prognostic risk model. The relationship between TMEscore and clinical and genomic characteristics was analyzed. The sample was divided into risk-H and risk-L groups based on the prognosis risk model of TMEscore, with significant differences in overall survival between the two groups (log rank p < 0.001). In terms of clinical features, the TMEscore is closely related to the T staging, Grade, and HPV. As for genomic characteristics, the genomic features of the Risk-H samples are a low expression of immune-related genes and high-frequency mutations of TP53 and CEP152. This model was validated in an external test set, in which the prognosis for Risk-H group and Risk-L group was also significantly different (log rank p = 0.017). A quantitative method of TME infiltration pattern is established, which may be a potential predictor of HNSC prognosis.

Integrating DNA methylation measures to improve clinical risk assessment: are we there yet? The case of BRCA1 methylation marks to improve clinical risk assessment of breast cancer

Current risk prediction models estimate the probability of developing breast cancer over a defined period based on information such as family history, non-genetic breast cancer risk factors, genetic information from high and moderate risk breast cancer susceptibility genes and, over the past several years, polygenic risk scores (PRS) from more than 300 common variants. The inclusion of additional data such as PRS improves risk stratification, but it is anticipated that the inclusion of epigenetic marks could further improve model performance accuracy. Here, we present the case for including information on DNA methylation marks to improve the accuracy of these risk prediction models, and consider how this approach contrasts genetic information, as identifying DNA methylation marks associated with breast cancer risk differs inherently according to the source of DNA, approaches to the measurement of DNA methylation, and the timing of measurement. We highlight several DNA-methylation-specific challenges that should be considered when incorporating information on DNA methylation marks into risk prediction models, using BRCA1, a highly penetrant breast cancer susceptibility gene, as an example. Only after careful consideration of study design and DNA methylation measurement will prospective performance of the incorporation of information regarding DNA methylation marks into risk prediction models be valid.

Tumor Microenvironment Characterization in Glioblastoma Identifies Prognostic and Immunotherapeutically Relevant Gene Signatures

Tumor microenvironment (TME) cells are important elements in tumor tissue. There is increasing evidence that they have important clinical pathological significance in predicting tumor clinical outcomes and therapeutic effects. However, no systematic analysis of TME cell interactions in glioblastoma (GBM) has been reported. We systematically analyzed the transcriptional sequencing data of GBM to find an immune gene marker to predict the clinical results of GBM. First, we downloaded the expression profiles and clinical follow-up information of GBM from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). CIBERSORT was used to evaluate the infiltration mode of TME in 757 patients, systematically correlated TME phenotype with genomic characteristics and clinicopathological characteristics of GBM, defined four TME phenotypes, and TMEScore was constructed using algorithms such as random forest and principal component analysis. There is a significant correlation between TMEScore and age of onset. High TMEScore samples are characterized by immune activation, TGF pathway activation, and high expression of immune checkpoint genes, while low TMEScore samples are characterized by high-frequency IDH1 and MET mutations. Therefore, a comprehensive landscape depicting the TME characteristics of GBM may help explain GBM's response to immunotherapy and provide new strategies for cancer treatment. In this study, TMEScore can be used as a new prognostic marker to predict the survival of GBM patients, and as a potential predictor of immune checkpoint inhibitor response.