Identification of DNA methylation-driven genes in esophageal squamous cell carcinoma: a study based on The Cancer Genome Atlas

Analysis of methylation-driven genes for predicting the prognosis of patients with oral squamous cell carcinoma

Background

Oral squamous cell carcinoma (OSCC) is a highly aggressive malignancy that is characterized by early distant metastasis and poor prognosis. DNA methylation plays an important role in the etiology and pathogenesis of OSCC. This study aimed to identify methylation-driven genes through bioinformatics analysis as potential biomarkers for early diagnosis and prognostic assessment of OSCC.

Methods

Methylation data, RNA sequencing (RNA-seq) data and clinical prognosis information of OSCC patients were retrieved from The Cancer Genome Atlas (TCGA) database. The R packages MethylMix were employed to analyze the correlation between methylation status and corresponding gene expression in tumor and normal tissues to obtain methylation-driven genes. Univariate Cox regression analysis was developed to further screen methylation-driven genes associated with the prognosis of OSCC patients. Subsequently, multivariate Cox regression analysis was utilized to construct a linear prognostic risk prediction model. Furthermore, a combined survival analysis integrating methylation and gene expression was performed to investigate the prognostic value.

Results

A total of 374 differentially expressed methylation-driven genes were identified. Seven methylation-driven genes (BST2, KRT15, ZNF134, NT5E, GSTA7P, NAPRT, and GOLPH3L) were found to be significantly associated with patient prognosis. Additionally, four methylation-driven genes (BST2, KRT15, ZNF134 and NAPRT) were used to construct a linear prognostic risk prediction model for OSCC patients. Furthermore, a combined Kaplan-Meier survival analysis revealed that three methylation-driven genes (ZKSCAN7, MFF, ZNF134) alone can be used as independent prognostic markers or drug targets.

Conclusions

Our findings facilitate a better understanding of molecular mechanisms of OSCC and provide potential biomarkers of early diagnosis, precision treatment and prognosis evaluation.

SGLT2 is upregulated to acquire cisplatin resistance and SGLT2 inhibition reduces cisplatin resistance in hepatoblastoma

BACKGROUND

Cancer cells can alter glucose metabolism and regulate the expression of glucose transporters. Hepatoblastoma patients undergo cisplatin-based chemotherapy; however, 22.3% of patients develop cisplatin resistance and thus face a poor prognosis. We hypothesized that glucose transporters are associated with acquiring cisplatin resistance with increasing sugar intake inhibiting glucose transporters could reduce cisplatin resistance in hepatoblastoma patients.

METHODS

We established cisplatin-resistant HepG2 and HuH6 cells by continuous treatment with cisplatin. We evaluated the relationship between cisplatin resistance and glucose uptake. We used an expression array to select cisplatin-resistant associated glucose transporters and selected sodium-glucose cotransporter 2 (SGLT2). We used dapagliflozin as an SGLT2 inhibitor and evaluated glucose uptake and IC50 after dapagliflozin treatment in wild-type and resistant hepatoblastoma cells in vitro and in vivo.

RESULTS

We found a strong relationship between cisplatin resistance and glucose uptake. Additionally, SGLT2 was upregulated in resistant cells after cisplatin treatment. After dapagliflozin treatment, glucose uptake and cisplatin resistance decreased in resistant cells.

CONCLUSIONS

Cisplatin-resistant hepatoblastoma cells exhibited upregulated SGLT2 expression and activated glucose uptake to survive under cisplatin stress. SGLT2 inhibition decreased cellular resistance to cisplatin. SGLT2 inhibition with cisplatin therapy could be a novel therapeutic strategy for cisplatin-resistant hepatoblastoma patients.

Role of F-box proteins in human upper gastrointestinal tumors

Protein ubiquitination and degradation is an essential physiological process in almost all organisms. As the key participants in this process, the E3 ubiquitin ligases have been widely studied and recognized. F-box proteins, a crucial component of E3 ubiquitin ligases that regulates diverse biological functions, including cell differentiation, proliferation, migration, and apoptosis by facilitating the degradation of substrate proteins. Currently, there is an increasing focus on studying the role of F-box proteins in cancer. In this review, we present a comprehensive overview of the significant contributions of F-box proteins to the development of upper gastrointestinal tumors, highlighting their dual roles as both carcinogens and tumor suppressors. We delve into the molecular mechanisms underlying the involvement of F-box proteins in upper gastrointestinal tumors, exploring their interactions with specific substrates and their cross-talks with other key signaling pathways. Furthermore, we discuss the implications of F-box proteins in radiotherapy resistance in the upper gastrointestinal tract, emphasizing their potential as clinical therapeutic and prognostic targets. Overall, this review provides an up-to-date understanding of the intricate involvement of F-box proteins in human upper gastrointestinal tumors, offering valuable insights for the identification of prognostic markers and the development of targeted therapeutic strategies.

Regulatory network and targeted interventions for CCDC family in tumor pathogenesis

CCDC (coiled-coil domain-containing) is a coiled helix domain that exists in natural proteins. There are about 180 CCDC family genes, encoding proteins that are involved in intercellular transmembrane signal transduction and genetic signal transcription, among other functions. Alterations in expression, mutation, and DNA promoter methylation of CCDC family genes have been shown to be associated with the pathogenesis of many diseases, including primary ciliary dyskinesia, infertility, and tumors. In recent studies, CCDC family genes have been found to be involved in regulation of growth, invasion, metastasis, chemosensitivity, and other biological behaviors of malignant tumor cells in various cancer types, including nasopharyngeal carcinoma, lung cancer, colorectal cancer, and thyroid cancer. In this review, we summarize the involvement of CCDC family genes in tumor pathogenesis and the relevant upstream and downstream molecular mechanisms. In addition, we summarize the potential of CCDC family genes as tumor therapy targets. The findings discussed here help us to further understand the role and the therapeutic applications of CCDC family genes in tumors.

Genome-Wide DNA Methylation and Gene Expression Profiling Characterizes Molecular Subtypes of Esophagus Squamous Cell Carcinoma for Predicting Patient Survival and Immunotherapy Efficacy

Simple Summary

Esophageal squamous cell carcinoma (ESCC) represents roughly 85–90% of all esophageal carcinoma patients in China. Immunotherapy is used to treat an increasing number of ESCC patients in clinical practice. This study aims to understand the molecular heterogeneity and the tumor immune microenvironment of ESCC for designing novel immunotherapies to improve response and outcomes. We identified two molecular subtypes associated with prognosis, immune-related pathways, and tumor microenvironment. In an independent cohort of Chinese ESCC patients treated with immunotherapy, the response rate of the S1 subtype is significantly higher than the S2 subtype. These findings provide a new perspective on the molecular subtyping for ESCC and a biological rationale for novel therapeutic intervention in a specific subgroup of ESCC that could potentially be translated into clinical practice both diagnostically and therapeutically to benefit ESCC patients.

Abstract

Background: Immunotherapy is recently being used to treat esophageal squamous cell carcinoma (ESCC); however, response and survival benefits are limited to a subset of patients. A better understanding of the molecular heterogeneity and tumor immune microenvironment in ESCC is needed for improving disease management. Methods: Based on the DNA methylation and gene expression profiles of ESCC patients, we identify molecular subtypes of patients and construct a predictive model for subtype classification. The clinical value of molecular subtypes for the prediction of immunotherapy efficacy is assessed in an independent validation cohort of Chinese ESCC patients who receive immunotherapy. Results: We identify two molecular subtypes of ESCC (S1 and S2) that are associated with distinct immune-related pathways, tumor microenvironment and clinical outcomes. Accordingly, S2 subtype patients had a poorer prognosis. A 15-gene expression signature is developed to classify molecular subtypes with an overall accuracy of 94.7% (89/94, 95% CI: 0.880–0.983). The response rate of immunotherapy is significantly higher in the S1 subtype than in the S2 subtype patients (68.75% vs. 25%, p = 0.028). Finally, potential target drugs, including mitoxantrone, are identified for treating patients of the S2 subtype. Conclusions: Our findings demonstrated that the identified molecular subtypes constitute a promising prognostic and predictive biomarker to guide the clinical care of ESCC patients.

Detection of Prognostic Biomarkers for Hepatocellular Carcinoma through CircRNA-associated CeRNA Analysis

BACKGROUND AND AIMS

The prognosis of hepatocellular carcinoma (HCC) is extremely poor; therefore, there is an urgent need for novel prognostic molecular biomarkers of HCC. The current investigation utilized circular (circ)RNA-associated competing endogenous (ce)RNAs analysis in order to identify significant prognostic biomarkers of HCC.

METHODS

CircRNAs and mRNAs that were differentially expressed between normal and HCC tissues were identified. Their respective functions were predicted with Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. A nomogram was used for model verification.

RESULTS

A ceRNA network composed of differentially expressed circRNAs and mRNAs was constructed. Significant hub nodes in the ceRNA network were hsa_circ_0004662, hsa_circ_0005735, hsa_circ_0006990, hsa_circ_0018403 and hsa_circ_0100609. By using this information, a prognostic risk assessment tool was developed based on the expressions of seven genes (PLOD2, TARS, RNF19B, CCT2, RAN, C5orf30 and MCM10). Furthermore, multivariate Cox regression analysis revealed risk and T-stage parameters as independent prognostic factors. The nomograms that were constructed from risk and T-stage groups were used to further assess the prediction of HCC patient survival rates. The nomogram, which consisted of risk and T-stage scores assessment models, was found to be an independent factor for predicting prognosis of HCC.

CONCLUSIONS

Five circRNAs, including hsa_circ_0004662, hsa_circ_0005735, hsa_circ_0006990, hsa_circ_0018403 and hsa_circ_0100609, that may play key roles in the progression of HCC were identified. Seven gene signatures were identified, which were associated with the aforementioned circRNAs, including PLOD2, TARS, RNF19B, CCT2, RAN, C5orf30 and MCM10, all of which were significant genes involved in the pathophysiology of HCC. These genes may be used as a prognosticating tool in HCC patients.

FBXO17 Inhibits the Wnt/β-Catenin Pathway and Proliferation of Ishikawa Cells

Uterine corpus endometrial carcinoma (UCEC) is one of the most common types of cancer in women, and the incidence is rapidly increasing. Studies have shown that various signaling pathways serve crucial roles in the tumorigenesis of UCEC, amongst which the Wnt/β-catenin pathway is of great interest due to its crucial role in cell proliferation and the huge potential as a therapeutic target. In the present study, it was shown that FBXO17, which is a member of the F-box family, is abnormally downregulated in UCEC tissues compared with non-tumor endometrial tissues, and this was significantly associated with the clinical histological grade, as well as the abnormal proliferation of the UCEC cell line, Ishikawa, both in vitro and in vivo. Besides, the results suggested that FBXO17 may inhibit the Wnt/β-catenin signaling pathway and influence the expression of adhesion molecules, such as E-cadherin and N-cadherin in Ishikawa cells. In conclusion, these findings indicate that FBXO17 is a novel inhibitor of endometrial tumor development and it likely exerts effects via regulation of the Wnt signaling pathway.

DNA methylation markers in esophageal cancer: an emerging tool for cancer surveillance and treatment

Esophageal carcinoma (EC) is one of the most pervasive cancers in the world, with upwards of 500,000 new diagnoses, annually. Despite its prominence, advancements in the detection and treatment of EC have been marginal over the past 30 years and the survival rate continues to stay below 20%. This is due to the uncommonly heterogeneous presentation of EC which presents unprecedented challenges in improving patient survival and quality of care. However, distinct epigenetic alterations to the DNA methylome may provide an avenue to drastically improve the detection and treatment of EC. Specifically, the creation of novel biomarker panels that consist of EC-specific methylation markers have shown promise as a potential alternative to the more invasive, contemporary diagnostic methods. Additionally, growing insight into the biological and clinical properties of EC-specific methylation patterns have opened a window of opportunity for enhanced treatment; of growing interest is the application of "DNMT inhibitors" - a class of drugs which inhibit excessive methylation and have been shown to re-sensitize chemoresistant tumors. Here we provide a comprehensive review of the current advancements in EC DNA methylation to underscore a potential approach to its detection and treatment.

Identification of DNA methylation-driven genes and construction of a nomogram to predict overall survival in pancreatic cancer

Background

The incidence and mortality of pancreatic cancer (PC) has gradually increased. The aim of this study was to identify survival-related DNA methylation (DNAm)-driven genes and establish a nomogram to predict outcomes in patients with PC.

Methods

The gene expression, DNA methylation database, and PC clinical samples were downloaded from TCGA. DNAm-driven genes were identified by integrating analyses of gene expression and DNA methylation data. Survival-related DNAm-driven genes were screened via univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses to develop a risk score model for prognosis. Based on analyses of clinical parameters and risk score, a nomogram was built and validated. The independent cohort from GEO database were used for external validation.

Results

A total of 16 differentially expressed methylation-driven genes were identified. Based on LASSO Cox regression and multivariate Cox regression analysis, six genes (FERMT1, LIPH, LAMA3, PPP1R14D, NQO1, VSIG2) were chosen to develop the risk score model. In the Kaplan–Meier analysis, age, T stage, N stage, AJCC stage, radiation therapy history, tumor size, surgery type performed, pathological type, chemotherapy history, and risk score were potential prognostic factors in PC (P < 0.1). In the multivariate analysis, stage, chemotherapy, and risk score were significantly correlated to overall survival (P < 0.05). The nomogram was constructed with the three variables (stage, chemotherapy, and risk score) for predicting the 1-year, 2-year, and 3-year survival rates of PC patients. Nomogram performance was assessed by receiver operating characteristic (ROC) curves and calibration curves. 1-year, 2-year and 3-year AUC of nomogram model was 0.899, 0.765 and 0.776, respectively.

Conclusions

In our study, we successfully identified the six DNAm-driven genes (FERMT1, LIPH, LAMA3, PPP1R14D, NQO1, VSIG2) with a relationship to the outcomes of PC patients. The nomogram including stage, chemotherapy, and risk score could be used to predict survival in PC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-08097-w.

Genomic and epigenomic evolution of acquired resistance to combination therapy in esophageal squamous cell carcinoma

BACKGROUNDTargeted arterial infusion of verapamil combined with chemotherapy (TVCC) is an effective clinical interventional therapy for esophageal squamous cell carcinoma (ESCC), but multidrug resistance (MDR) remains the major cause of relapse or poor prognosis, and the underlying molecular mechanisms of MDR, temporal intratumoral heterogeneity, and clonal evolutionary processes of resistance have not been determined.METHODSTo elucidate the roles of genetic and epigenetic alterations in the evolution of acquired resistance during therapies, we performed whole-exome sequencing on 16 serial specimens from 7 patients with ESCC at every cycle of therapeutic intervention from 3 groups, complete response, partial response, and progressive disease, and we performed whole-genome bisulfite sequencing for 3 of these 7 patients, 1 patient from each group.RESULTSPatients with progressive disease exhibited a substantially higher genomic and epigenomic temporal heterogeneity. Subclonal expansions driven by the beneficial new mutations were observed during combined therapies, which explained the emergence of MDR. Notably, SLC7A8 was identified as a potentially novel MDR gene, and functional assays demonstrated that mutant SLC7A8 promoted the resistance phenotypes of ESCC cell lines. Promoter methylation dynamics during treatments revealed 8 drug resistance protein-coding genes characterized by hypomethylation in promoter regions. Intriguingly, promoter hypomethylation of SLC8A3 and mutant SLC7A8 were enriched in an identical pathway, protein digestion and absorption, indicating a potentially novel MDR mechanism during treatments.CONCLUSIONOur integrated multiomics investigations revealed the dynamics of temporal genetic and epigenetic inter- and intratumoral heterogeneity, clonal evolutionary processes, and epigenomic changes, providing potential MDR therapeutic targets in treatment-resistant patients with ESCC during combined therapies.FUNDINGNational Natural Science Foundation of China, Science Foundation of Peking University Cancer Hospital, CAMS Innovation Fund for Medical Sciences, Major Program of Shenzhen Bay Laboratory, Guangdong Basic and Applied Basic Research Foundation, and the third round of public welfare development and reform pilot projects of Beijing Municipal Medical Research Institutes.

Evaluation of the Impact of Intratumoral Heterogeneity of Esophageal Cancer on Pathological Diagnosis and P16 Methylation and the Representativity of Endoscopic Biopsy

Background

P16 methylation is expected to be potential diagnostic and therapeutic targets for esophageal cancer (EC). The intratumoral heterogeneity (ITH) of EC has been mentioned but has not been quantitatively measured yet. We aimed to clarify the impact of ITH on pathological diagnosis and P16 methylation, and the concordance between endoscopic biopsy and the corresponding surgically resected tissue.

Methods

We designed a systematic sampling method (SSM) compared with a general sampling method (GSM) to obtain EC tumor tissue, tumor biopsy, and normal squamous epithelium biopsy. MethyLight assay was utilized to test P16 methylation. All specimens obtained by the SSM were pathologically diagnosed.

Results

A total of 81 cases were collected by the GSM, and 91.4% and 8.6% of them were esophageal squamous cell carcinomas (ESCCs) and esophageal adenocarcinomas (EADs), respectively. Nine SSM cases were 100.0% ESCCs. The positive rates of P16 methylation of the GSM tumor and normal tissues were 63.0% (51/81) and 32.1% (26/81), respectively. For SSM samples, tumor tissues were 100.0% (40/40) EC and 85.0% (34/40) P16 methylated; tumor biopsy was 64.4% (29/45) diagnosed of EC and 68.9% P16 methylated; the corresponding normal biopsies were 15.7% (8/51) dysplasia and 54.9% (28/51) P16 methylated. The concordance of pathological diagnosis and P16 methylation between tumor biopsy and the corresponding tumor tissue was 75.0% and 62.5%, respectively.

Conclusion

The SSM we designed was efficient in measuring the ITH of EC. We found inadequate concordance between tumor biopsy and tissue in pathological diagnosis and P16 methylation.

Identification of 4-methylation driven genes based prognostic signature in thyroid cancer: an integrative analysis based on the methylmix algorithm

Thyroid cancer (TC) is known with a high rate of persistence and recurrence. We aimed to develop a prognostic signature to monitor and assess the survival of TC patients. mRNA expression and methylation data were downloaded from the TCGA database. Then, R package methylmix was applied to construct a mixed model was used to identify methylation-driven genes (MDGs) according to the methylation levels. Furthermore, an MDGs based prognostic signature and predictive nomogram were constructed according to the analysis of univariate and multivariate Cox regression. Totally 62 methylation-driven genes that were mainly enriched in substrate-dependent cell migration, cellular response to mechanical stimulus, et al. were found in TC tissues. aldolase C (AldoC), C14orf62, dishevelled 1 (DVL1), and protein tyrosine phosphatase receptor type C (PTPRC) were identified to be significantly related to patients' survival, and may serve as independent prognostic biomarkers for TC. Additionally, the prognostic methylation signature and a novel prognostic, predictive nomogram was established based on the methylation level of 4 MDGs. In this study, we developed a 4-MDGs based prognostic model, which might be the potential predictors for the survival rate of TC patients, and this findings might provide a novel sight for accurate monitoring and prognosis assessment.

G9a Regulates Cell Sensitivity to Radiotherapy via Histone H3 Lysine 9 Trimethylation and CCDC8 in Lung Cancer

Purpose

To investigate the role and underlying mechanism of G9a and CCDC8 in lung cancer radioresistance.

Methods

Western blotting assays were used for G9a, CCDC8, H3K9me3 expression detection. MTT assays and clone formation assays were used for measuring cell proliferation activities. Flow cytometry assays were used for cell apoptosis detection. The enrichment of H3K9me3 in CCDC8 promoter was measured by chromatin immunoprecipitation assay.

Results

G9a and G9a-mediated H3K9me3 are upregulated in radioresistant lung cancer cells (A549/IR cell and XWLC-05/IR cell). Blocking G9a not only promotes radiosensitivity of A549/IR cell and XWLC-05/IR cell but also reduces aggressive behavior of radioresistant A549 cell/IR and XWLC-05/IR cell. In addition, G9a-controlled H3K9me3 is able to binding to the promoter of tumor suppressor gene CCDC8 and suppresses CCDC8 expression. CCDC8 dysregulation is responsible for G9a-mediated radioresistance of A549/IR cell and XWLC-05/IR cell.

Conclusion

G9a and H3K9me3 contribute to the lung cancer radioresistance via modulating CCDC8 expression.

Novel prognostic prediction model constructed through machine learning on the basis of methylation-driven genes in kidney renal clear cell carcinoma

Kidney renal clear cell carcinoma (KIRC) is a common tumor with poor prognosis and is closely related to many aberrant gene expressions. DNA methylation is an important epigenetic modification mechanism and a novel research target. Thus, exploring the relationship between methylation-driven genes and KIRC prognosis is important. The methylation profile, methylation-driven genes, and methylation characteristics in KIRC was revealed through the integration of KIRC methylation, RNA-seq, and clinical information data from The Cancer Genome Atlas. The Lasso regression was used to establish a prognosis model on the basis of methylation-driven genes. Then, a trans-omics prognostic nomogram was constructed and evaluated by combining clinical information and methylated prognosis model. A total of 242 methylation-driven genes were identified. The Gene Ontology terms of these methylation-driven genes mainly clustered in the activation, adhesion, and proliferation of immune cells. The methylation prognosis prediction model that was established using the Lasso regression included four genes in the methylation data, namely, FOXI2, USP44, EVI2A, and TRIP13. The areas under the receiver operating characteristic curve of 1-, 3-, and 5-year survival rates were 0.810, 0.824, and 0.799, respectively, in the training group and 0.794, 0.752, and 0.731, respectively, in the testing group. An easy trans-omics nomogram was successfully established. The C-indices of the nomogram in the training and the testing groups were 0.8015 and 0.8389, respectively. The present study revealed the overall perspective of methylation-driven genes in KIRC and can help in the evaluation of the prognosis of KIRC patients and provide new clues for further study.

Overexpression of FBXO17 Promotes the Proliferation, Migration and Invasion of Glioma Cells Through the Akt/GSK-3β/Snail Pathway

FBXO17 is a newly studied F-box protein associated with high-grade glioma. However, its exact role in glioma remains unclear. In the present study, we aimed to investigate the role of FBXO17 in glioma both in vitro and in vivo and explore the underlying mechanism. Our results showed that FBXO17 mRNA and protein levels were upregulated in glioma cells including U87, U251, SHG44, and U-118-MG cells as compared to the HA1800 cells. Downregulation of FBXO17 significantly suppressed the cellular behaviors of glioma cells including cell proliferation, migration, and invasion. In addition, FBXO17 knockdown induced E-cadherin expression and inhibited N-cadherin and vimentin expression at mRNA and protein levels in glioma cells. In contrast, overexpression of FBXO17 promoted cell proliferation, migration, invasion and EMT process. Furthermore, FBXO17 regulated the Akt/GSK-3β/snail signaling pathway in glioma cells with significant changes in the expression levels of p-Akt, p-GSK-3β and snail. Additionally, inhibition of Akt by LY294002 reversed the effects of FBXO17 overexpression on cellular behaviors of glioma cells. Finally, in vivo mouse xenograft assay proved that downregulation of FBXO17 suppresses the tumorigenesis of glioma. In conclusion, these findings demonstrated that FBXO17 acted as a promotor of glioma development via modulating Akt/GSK-3β/snail signaling pathway.

Integrated analysis of DNA methylation and mRNA expression profiles to identify key genes in head and neck squamous cell carcinoma

DNA methylation has been demonstrated to play significant roles in the etiology and pathogenesis of head and neck squamous cell carcinoma (HNSCC). In the present study, methylation microarray dataset (GSE87053) and gene expression microarray dataset (GSE23558) were downloaded from GEO database and analyzed through R language. A total of 255 hypermethylated-downregulated genes and 114 hypomethylated-upregulated genes were finally identified. Functional enrichment analyses were performed and a comprehensive protein–protein interaction (PPI) network was constructed. Subsequently, the top ten hub genes selected by Cytoscape software were subjected to further analyses. It was illustrated that the expression level of CSF2, CTLA4, ETS1, PIK3CD, and CFTR was intimately associated with HNSCC. Survival analysis suggested that CTLA4 and FGFR2 could serve as effective independent prognostic biomarkers for HNSCC patients. Overall, our study lay a groundwork for further investigation into the underlying molecular mechanisms in HNSCC carcinogenesis, providing potential biomarkers and therapeutic targets for HNSCC.

Systematic analysis of the expression and prognosis relevance of FBXO family reveals the significance of FBXO1 in human breast cancer

Background

Breast cancer (BC) remains a prevalent and common form of cancer with high heterogeneity. Making efforts to explore novel molecular biomarkers and serve as potential disease indicators, which is essential to effectively enhance the prognosis and individualized treatment of BC. FBXO proteins act as the core component of E3 ubiquitin ligase, which play essential regulators roles in multiple cellular processes. Recently, research has indicated that FBXOs also play significant roles in cancer development. However, the molecular functions of these family members in BC have not been fully elucidated.

Methods

In this research, we investigated the expression data, survival relevance and mutation situation of 10 FBXO members (FBXO1, 2, 5, 6, 16, 17, 22, 28, 31 and 45) in patients with BC from the Oncomine, GEPIA, HPA, Kaplan–Meier Plotter, UALCAN and cBioPortal databases. The high transcriptional levels of FBXO1 in different subtypes of BC were verified by immunohistochemical staining and the specific mutations of FBXO1 were obtained from COSMIC database. Top 10 genes with the highest correlation to FBXO1 were identified through cBioPortal and COXPRESdb tools. Additionally, functional enrichment analysis, PPI network and survival relevance of FBXO1 and co-expressed genes in BC were obtained from DAVID, STRING, UCSC Xena, GEPIA, bc-GenExMiner and Kaplan–Meier Plotter databases. FBXO1 siRNAs were transfected into MCF-7 and MDA-MB-231 cell lines. Expression of FBXO1 in BC cell lines was detected by western-blot and RT-qPCR. Cell proliferation was detected by using CCK-8 kit and colony formation assay. Cell migration was detected by wound‐healing and transwell migration assay.

Results

We found that FBXO2, FBXO6, FBXO16 and FBXO17 were potential favorable prognostic factors for BC. FBXO1, FBXO5, FBXO22, FBXO28, FBXO31 and FBXO45 may be the independent poor prognostic factors for BC. All of them were correlated to clinicopathological staging. Moreover, knockdown of FBXO1 in MCF7 and MDA-MB-231 cell lines resulted in decreased cell proliferation and migration in vitro. We identified that FBXO1 was an excellent molecular biomarker and therapeutic target for different molecular typing of BC.

Conclusion

This study implies that FBXO1, FBXO2, FBXO5, FBXO6, FBXO16, FBXO17, FBXO22, FBXO28, FBXO31 and FBXO45 genes are potential clinical targets and prognostic biomarkers for patients with different molecular typing of BC. In addition, the overexpression of FBXO1 is always found in breast cancer and predicts disadvantageous prognosis, implicating it could as an appealing therapeutic target for breast cancer patients.

Classification of Homo sapiens gene behavior using linear discriminant analysis fused with minimum entropy mapping

Classification of Homo sapiens gene behavior employing computational biology is a recent research trend. But monitoring gene activity profile and genetic behavior from the alphabetic DNA sequence using a non-invasive method is a tremendous challenge in functional genomics. The present paper addresses such issue and attempts to differentiate Homo sapiens genes using linear discriminant analysis (LDA) method. Annotated protein coding sequences of Homo sapiens genes, collected from NCBI, are taken as test samples. Minimum entropy-based mapping (MEM) technique assists to extract highest information from the numerical DNA sequences. The proposed LDA technique has successfully classified Homo sapiens genes based on the following features: composition of hydrophilic amino acids, dominance of arginine amino acid, and magnitude and size of individual amino acids. The proposed algorithm is successfully tested on 84 Homo sapiens healthy and cancer genes of the prostate and breast cells. Classification performance of the proposed LDA technique is judged by sensitivity (89.12%), specificity (91.9%), accuracy (90.87%), F1 score (92.03%), Matthews' correlation coefficients (81.04%), and miss rate (9.12%), and it outperforms other four existing classifiers. The results are cross-validated through Rayleigh PDF and mutual information technique. Fisher test, 2-sample T-test, and relative entropy test are considered to verify the efficacy of the present classifier.

Six Novel Biomarkers for Diagnosis and Prognosis of Esophageal squamous cell carcinoma: validated by scRNA-seq and qPCR

Esophageal squamous cell carcinoma (ESCC) is one of the most common cancers worldwide. ESCC has a generally poor prognosis and there is a lack of available biomarkers for diagnosis and prognosis. The aim of the study was to identify novel biomarkers for ESCC. We screened the overlapping differentially expressed genes (DEGs) acquired from six Gene Expression Omnibus (GEO) ESCC datasets and The Cancer Genome Atlas (TCGA) ESCC datasets. Subsequently, protein-protein interaction network analysis was performed to identify the key hub genes. Then, Kaplan Meier survival and receiver operating curve (ROC) analysis were utilized to clarify the diagnostic and prognostic role of these hub genes. The UALCAN database, single cell RNA sequencing (scRNA-seq) and real-time quantitative PCR (qPCR) were performed to confirm the expression levels of identified hub genes. Finally, immune infiltration analysis was conducted to investigate the role of these genes in the pathogenesis of ESCC. The results showed that PBK, KIF2C, NUF2, KIF20A, RAD51AP1, and DEPDC1 effectively distinguish ESCC tissues from normal samples, and all of them were significantly correlated with overall survival. The results of scRNA-seq and qPCR indicated that the expression levels of hub genes in ESCC were significantly higher than in normal cells or tissues. Further immune infiltration analysis showed that infiltration of dendritic cells was significantly negatively correlated with PBK, KIF2C, NUF2, RAD51AP1, and DEPDC1 expression levels. In conclusion, our results suggest that PBK, KIF2C, NUF2, KIF20A, RAD51AP1 and DEPDC1 are all potential biomarkers for ESCC diagnosis and prognosis may also be potential therapeutic targets for ESCC.

A model for predicting prognosis in patients with esophageal squamous cell carcinoma based on joint representation learning

Esophageal squamous cell carcinoma (ESCC) is one of the deadliest cancer types with a poor prognosis due to the lack of symptoms in the early stages and a delayed diagnosis. The present study aimed to identify the risk factors significantly associated with prognosis and to search for novel effective diagnostic modalities for patients with early-stage ESCC. mRNA and methylation data of patients with ESCC and the corresponding clinical information were downloaded from The Cancer Genome Atlas (TCGA) database, and the representation features were screened using deep learning autoencoder. The univariate Cox regression model was used to select the prognosis-related features from the representation features. K-means clustering was used to cluster the TCGA samples. Support vector machine classifier was constructed based on the top 75 features mostly associated with the risk subgroups obtained from K-means clustering. Two ArrayExpress datasets were used to verify the reliability of the obtained risk subgroups. The differentially expressed genes and methylation genes (DEGs and DMGs) between the risk subgroups were analyzed, and pathway enrichment analysis was performed. A total of 500 representation features were produced. Using K-means clustering, the TCGA samples were clustered into two risk subgroups with significantly different overall survival rates. Joint multimodal representation strategy, which showed a good model fitness (C-index=0.760), outperformed early-fusion autoencoder strategy. The joint representation learning-based classification model had good robustness. A total of 1,107 DEGs and 199 DMGs were screened out between the two risk subgroups. The DEGs were involved in 70 pathways, the majority of which were correlated with metastasis and proliferation of various cancer types, including cytokine-cytokine receptor interaction, cell adhesion molecules PPAR signaling pathway, pathways in cancer, transcriptional misregulation in cancer and ECM-receptor interaction pathways. The two survival subgroups obtained via the joint representation learning-based model had good robustness, and had prognostic significance for patients with ESCC.

Analysis of DNA methylation-driven genes for predicting the prognosis of patients with colorectal cancer

Aberrant promoter methylation and ensuing abnormal gene expression are important epigenetic mechanisms that contribute to colorectal oncogenesis. Yet, the prognostic significance of such methylation-driven genes in colorectal cancer (CRC) remains obscure. Herein, a total of 181 genes were identified as the methylation-driven molecular features of CRC by integrated analysis of the expression profiles and the matched DNA methylation data from The Cancer Genome Atlas (TCGA) database. Among them, a five-gene signature (POU4F1, NOVA1, MAGEA1, SLCO4C1, and IZUMO2) was developed as a risk assessment model for predicting the clinical outcomes in CRC. The Kaplan-Meier analysis and Harrell's C index demonstrated that the risk assessment model significantly distinguished the patients in high or low-risk groups (p-value < 0.0001 log-rank test, HR: 2.034, 95% CI: 1.419-2.916, C index: 0.655). The sensitivity and specificity were validated by the receiver operating characteristic (ROC) analysis. Furthermore, different pharmaceutical treatment responses were observed between the high-risk and low-risk groups. Indeed, the methylation-driven gene signature could act as an independent prognostic evaluation biomarker for assessing the OS of CRC patients and guiding the pharmaceutical treatment. Compared with known biomarkers, the methylation-driven gene signature could reveal cross-omics molecular features for improving clinical stratification and prognosis.

Aberrant DNA Methylation of ABC Transporters in Cancer

ATP-binding cassette (ABC) transporters play a crucial role in multidrug resistance (MDR) of cancers. They function as efflux pumps, resulting in limited effectiveness or even failure of therapy. Increasing evidence suggests that ABC transporters are also involved in tumor initiation, progression, and metastasis. Tumors frequently show multiple genetic and epigenetic abnormalities, including changes in histone modification and DNA methylation. Alterations in the DNA methylation status of ABC transporters have been reported for a variety of cancer types. In this review, we outline the current knowledge of DNA methylation of ABC transporters in cancer. We give a brief introduction to structure, function, and gene regulation of ABC transporters that have already been investigated for their DNA methylation status in cancer. After giving an overview of the applied methodologies and the CpGs analyzed, we summarize and discuss the findings on aberrant DNA methylation of ABC transporters by cancer types. We conclude our review with the discussion of the potential to target aberrant DNA methylation of ABC transporters for cancer therapy.

DNA methylation data-based prognosis-subtype distinctions in patients with esophageal carcinoma by bioinformatic studies

Esophageal carcinoma (ESCA) is caused by the accumulation of genetic and epigenetic alterations in esophageal mucosa. Of note, the earliest and the most frequent molecular behavior in the complicated pathogenesis of ESCA is DNA methylation. In the present study, we downloaded data of 178 samples from The Cancer Genome Atlas (TCGA) database to explore specific DNA methylation sites that affect prognosis in ESCA patients. Consequently, we identified 1,098 CpGs that were significantly associated with patient prognosis. Hence, these CpGs were used for consensus clustering of the 178 samples into seven clusters. Specifically, the samples in each group were different in terms of age, gender, tumor stage, histological type, metastatic status, and patient prognosis. We further analyzed 1,224 genes in the corresponding promoter regions of the 1,098 methylation sites, and enriched these genes in biological pathways with close correlation to cellular metabolism, enzymatic synthesis, and mitochondrial autophagy. In addition, nine representative specific methylation sites were screened using the weighted gene coexpression network analysis. Finally, a prognostic prediction model for ESCA patients was built in both training and validation cohorts. In summary, our study revealed that classification based on specific DNA methylation sites could reflect ESCA heterogeneity and contribute to the improvement of individualized treatment and precise prognostic prediction.

The Pathologic and Molecular Landscape of Esophageal Squamous Cell Carcinogenesis

Esophageal squamous cell carcinoma represents the most common histotype of epithelial neoplasm occurring within esophageal mucosa worldwide. Despite the comprehensive molecular characterization of this entity, to date no significant targeted therapy has been introduced into clinical practice. In this review, we describe the molecular landscape of esophageal squamous cell carcinoma based on the most recent literature. Moreover, we focus on other rare variants and on the relationship with head and neck squamous cell carcinomas.

Identification of the prognostic value of immune gene signature and infiltrating immune cells for esophageal cancer patients

BACKGROUND

Esophageal cancer (ESCA) is one of the deadliest solid malignancies with worse survival rate worldwide. Here, we aimed to establish an immune-gene prognostic signature for predicting patients' survival and providing accurate targets for personalized therapy or immunotherapy.

METHODS

Gene expression profile of patients with ESCA were download from The Cancer Genome Atlas (TCGA) database (dataset 1: n = 159) and immune-related genes from the ImmPORT database. Dataset 1 was subdivided into two groups (dataset 2: n = 80; dataset 3: n = 79). Kaplan-Meier and receiver operating characteristic (ROC) curves were plotted to validate the predictive effect of the prognostic signature on the three datasets. TIMER and CIBERSORT analysis were used to evaluate the correlation between the prognostic signature and infiltrating immune cells.

RESULTS

We constructed a prognostic signature composed of six immune genes (HSPA6, S100A12, FABP3, DKK1, OSM and NR2F2). Kaplan-Meier curves validated the good predictive ability of the prognostic signature in datasets 1, 2 and 3 (P = 0.0034, P = 0.0081, and P = 0.0363, respectively). The area under the curve (AUC) of the ROC curves validated the predictive accuracy of the immune signature (AUCs = 0.757, 0.800, and 0.701, respectively). We also revealed the good prognostic value of the immune cells, including activated memory CD4 T cells, T follicular helper cells and monocytes. Potential target drugs, including Olopatadine and Amlexanox, were identified for clinical therapies to improve patients' survival outcomes.

CONCLUSION

Our study indicated that the immune-related prognostic signature could serve as a novel biomarker for predicting patients' prognosis and providing new immunotherapy targets in ESCA.

Methylation-driven genes and their prognostic value in cervical squamous cell carcinoma

Background

Abnormal gene methylation is crucial for tumor progression. This study explored a cluster of methylation-driven genes involved in cervical squamous cell carcinoma (CESC).

Methods

The data on RNA expression, methylation and clinical outcomes of CESC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Protein-protein interaction (PPI) network was constructed. Gene Ontology (GO) and KEGG analyses were performed to identify the biological functions of methylation-driven genes, and univariable and multivariate Cox analyses to screen out the key prognostic genes. A risk signature was established and its predictive value was evaluated with Kaplan-Meier and ROC curves. The key genes were further investigated by Cox regression analyses, gene set enrichment analysis (GSEA), and methylation site analysis. Additionally, "rms" package was used for establishing nomogram and calibrate curve.

Results

We found 144 differentially expressed methylation-driven genes. A risk model was constructed with three key prognostic genes (ITGA5, HHEX and S1PR4). The risk score was an independent risk factor for CESC prognosis. Lowly-expressed and hypermethylated ITGA5, highly-expressed and hypomethylated HHEX and S1PR4 were associated with better CESC prognosis. The methylation sites and biological functions enriched in ITGA5, HHEX and S1PR4 were uncovered. Additionally, the nomogram also validated the performance of risk model.

Conclusions

Methylation-driven ITGA5, HHEX and S1PR4 are associated with CESC development. The three genes might serve as potential targets in the treatment of CESC.

Identification of methylation-driven genes related to the prognosis of papillary renal cell carcinoma: a study based on The Cancer Genome Atlas

Background

Aberrant DNA methylation patterns are involved in the pathogenesis of papillary renal cell carcinoma (pRCC). This study aimed to investigate the potential of methylation-driven genes as biomarkers in determining the prognosis of pRCC by bioinformatics analysis.

Methods

DNA methylation and transcriptome profiling data were downloaded from The Cancer Genome Atlas database. Methylation-driven genes (MDGs) were obtained using MethylMix R package. A Cox regression model was used to screen for pRCC prognosis-related MDGs, and a linear risk model based on MDG methylation profiles was constructed. A combined methylation and gene expression survival analysis was performed to further explore the prognostic value of MDGs independently.

Results

A total of 31 MDGs were obtained. Univariate and multivariate Cox regression analysis identified eight genes (CASP1, CD68, HOXD3, HHLA2, HOXD9, HOXA10-AS, TMEM71, and PLA2G16), which were used to construct a predictive model associated with overall survival in pRCC patients. Combined DNA methylation and gene expression survival analysis revealed that C19orf33, GGT6, GIPC2, HHLA2, HOXD3, HSD17B14, PLA2G16, and TMEM71 were significantly associated with patients’ survival.

Conclusion

Through the analysis of MDGs in pRCC, this study identified potential biomarkers for precision treatment and prognosis prediction, and provided the basis for future research into the molecular mechanism of pRCC.

Identification of candidate aberrantly methylated and differentially expressed genes in Esophageal squamous cell carcinoma

Aberrant methylated genes (DMGs) play an important role in the etiology and pathogenesis of esophageal squamous cell carcinoma (ESCC). In this study, we aimed to integrate three cohorts profile datasets to ascertain aberrant methylated-differentially expressed genes and pathways associated with ESCC by comprehensive bioinformatics analysis. We downloaded data of gene expression microarrays (GSE20347, GSE38129) and gene methylation microarrays (GSE52826) from the Gene Expression Omnibus (GEO) database. Aberrantly differentially expressed genes (DEGs) were obtained by GEO2R tool. The David database was then used to perform Gene ontology (GO) analysis and Kyoto Encyclopedia of Gene and Genome pathway enrichment analyses on selected genes. STRING and Cytoscape software were used to construct a protein-protein interaction (PPI) network, then the modules in the PPI networks were analyzed with MCODE and the hub genes chose from the PPI networks were verified by Oncomine and TCGA database. In total, 291 hypomethylation-high expression genes and 168 hypermethylation-low expression genes were identified at the screening step, and finally found six mostly changed hub genes including KIF14, CDK1, AURKA, LCN2, TGM1, and DSG1. Pathway analysis indicated that aberrantly methylated DEGs mainly associated with the P13K-AKT signaling, cAMP signaling and cell cycle process. After validation in multiple databases, most hub genes remained significant. Patients with high expression of AURKA were associated with shorter overall survival. To summarize, we have identified six feasible aberrant methylated-differentially expressed genes and pathways in ESCC by bioinformatics analysis, potentially providing valuable information for the molecular mechanisms of ESCC. Our data combined the analysis of gene expression profiling microarrays and gene methylation profiling microarrays, simultaneously, and in this way, it can shed a light for screening and diagnosis of ESCC in future.

Diagnostic model of combined ceRNA and DNA methylation related genes in esophageal carcinoma

Esophageal cancer is a common malignant tumor in the world, and the aim of this study was to screen key genes related to the development of esophageal cancer using a variety of bioinformatics analysis tools and analyze their biological functions. The data of esophageal squamous cell carcinoma from the Gene Expression Omnibus (GEO) were selected as the research object, processed and analyzed to screen differentially expressed microRNAs (miRNAs) and differential methylation genes. The competing endogenous RNAs (ceRNAs) interaction network of differentially expressed genes was constructed by bioinformatics tools DAVID, String, and Cytoscape. Biofunctional enrichment analysis was performed using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). The expression of the screened genes and the survival of the patients were verified. By analyzing GSE59973 and GSE114110, we found three down-regulated and nine up-regulated miRNAs. The gene expression matrix of GSE120356 was calculated by Pearson correlation coefficient, and the 11696 pairs of ceRNA relation were determined. In the ceRNA network, 643 lncRNAs and 147 mRNAs showed methylation difference. Functional enrichment analysis showed that these differentially expressed genes were mainly concentrated in the FoxO signaling pathway and were involved in the corresponding cascade of calcineurin. By analyzing the clinical data in The Cancer Genome Atlas (TCGA) database, it was found that four lncRNAs had an important impact on the survival and prognosis of esophageal carcinoma patients. QRT-PCR was also conducted to identify the expression of the key lncRNAs (RNF217-AS1, HCP5, ZFPM2-AS1 and HCG22) in ESCC samples. The selected key genes can provide theoretical guidance for further research on the molecular mechanism of esophageal carcinoma and the screening of molecular markers.

Whole slide images reflect DNA methylation patterns of human tumors

DNA methylation is an important epigenetic mechanism regulating gene expression and its role in carcinogenesis has been extensively studied. High-throughput DNA methylation assays have been used broadly in cancer research. Histopathology images are commonly obtained in cancer treatment, given that tissue sampling remains the clinical gold-standard for diagnosis. In this work, we investigate the interaction between cancer histopathology images and DNA methylation profiles to provide a better understanding of tumor pathobiology at the epigenetic level. We demonstrate that classical machine learning algorithms can associate the DNA methylation profiles of cancer samples with morphometric features extracted from whole slide images. Furthermore, grouping the genes into methylation clusters greatly improves the performance of the models. The well-predicted genes are enriched in key pathways in carcinogenesis including hypoxia in glioma and angiogenesis in renal cell carcinoma. Our results provide new insights into the link between histopathological and molecular data.

Methylation‑driven genes PMPCAP1, SOWAHC and ZNF454 as potential prognostic biomarkers in lung squamous cell carcinoma

Of the different types of lung cancer, lung squamous cell cancer (LUSC) has the second highest rates of morbidity and mortality, which have been increasing in recent years. Epigenetic abnormalities may serve as potential biomarkers and diagnostic and/or therapeutic targets, which may help to monitor and improve the prognosis of patients with cancer. In the present study, data were obtained from The Cancer Genome Atlas database and survival and joint survival analyses were conducted using the R MethylMix package. Peptidase, mitochondrial processing a subunit pseudogene 1 (PMPCAP1), sosondowah ankyrin repeat domain family member C (SOWAHC) and zinc finger protein (ZNF) 454 were identified as independent prognosis‑related hub methylation‑driven genes (MDGs). Of these three genes, PMPCAP1 and SOWAHC, characterized by hypomethylation and high expression levels, were associated with poor prognosis in patients with LUSC, whilst ZNF454 was associated with an improved prognosis. In addition, pathway enrichment analysis suggested that PMPCAP1, SOWAHC and ZNF454 were primarily involved in gene expression or transcription pathways. Furthermore, 5, 1 and 10 key methylation sites of PMPCAP1, SOWAHC and ZNF454, respectively, were confirmed to be significantly relevant to gene expression, establishing a basis for further investigation into the mechanisms and more precise targets of these 3 genes. In conclusion, the MDGs PMPCAP1, SOWAHC and ZNF454 may be potential prognostic biomarkers of LUSC for guiding diagnosis and therapy options, as well as providing a theoretical basis for further investigation.

Folate inhibits miR-27a-3p expression during cervical carcinoma progression and oncogenic activity in human cervical cancer cells

BACKGROUND

Folate deficiency has been long implicated in cancer development. Although the role of folate in preventing cervical cancer is still unclear, emerging evidence shows that microRNAs (miRs) have great influence on tumor cell migration and invasion.

OBJECTIVES

The purpose of this study was to conduct an integrated analysis of miR expression in squamous cell carcinoma tissues with adequate or deficient serum folate. Further, study conducted tissue validation and functional analysis of miRs to uncover novel pathogenic mechanisms on the role of folate in squamous cell carcinoma (SCC).

MATERIALS AND METHODS

miR expression profiles were obtained from five paired primary SCC tumors with sufficient or deficient serum folate levels through Affymetrix GeneChip microRNA 4.0. This was followed by an integrated bioinformatics analysis and expanded sample size to verify core miRs by molecular biological validation. HeLa and SiHa cells with different concentrations of folate were used to clarify the roles of miR-27a on cell proliferation, migration, and invasion. MiR-27a expression was measured by the quantitative real-time polymerase chain reaction. Cell counting proliferation, wound healing, and transwell invasion assays were used to determine cell survival, proliferation, migration, and invasion abilities, respectively.

RESULTS

Our study found increasing miR-27a expression in serum of normal, high-grade squamous intraepithelial lesion (HSIL), and SCC tissues (in order of magnitude), which trend was negatively correlated with serum folate content. Further, there were significant differences in cellular miR-27a expression between 200 nM and 500 nM folate concentrations, with higher folate concentrations showing lower proliferation, migration, and invasion in SCC. Finally, miR-27a promoted proliferation and invasion in HeLa cells, whereas a miR-27a inhibitor blocked cell proliferation and invasion.

CONCLUSION

There is a significant association between miR-27a expression and folate during cervical carcinoma progression. Therefore, miR-27a could be used as a new biomarker for SCC diagnosis and prediction, suggesting a new therapeutic strategy for SCC treatment.

Integrative analysis of DNA methylation-driven genes for the prognosis of lung squamous cell carcinoma using MethylMix

Background: DNA methylation acts as a key component in epigenetic modifications of genomic function and functions as disease-specific prognostic biomarkers for lung squamous cell carcinoma (LUSC). This present study aimed to identify methylation-driven genes as prognostic biomarkers for LUSC using bioinformatics analysis. Materials and Methods: Differentially expressed RNAs were obtained using the edge R package from 502 LUSC tissues and 49 adjacent non-LUSC tissues. Differentially methylated genes were obtained using the limma R package from 504 LUSC tissues and 69 adjacent non-LUSC tissues. The methylation-driven genes were obtained using the MethylMix R package from 500 LUSC tissues with matched DNA methylation data and gene expression data and 69 non-LUSC tissues with DNA methylation data. Gene ontology and ConsensusPathDB pathway analysis were performed to analyze the functional enrichment of methylation-driven genes. Univariate and multivariate Cox regression analyses were performed to identify the independent effect of differentially methylated genes for predicting the prognosis of LUSC. Results: A total of 44 methylation-driven genes were obtained. Univariate and multivariate Cox regression analyses showed that twelve aberrant methylated genes (ATP6V0CP3, AGGF1P3, RP11-264L1.4, HIST1H4K, LINC01158, CH17-140K24.1, CTC-523E23.14, ADCYAP1, COX11P1, TRIM58, FOXD4L6, CBLN1) were entered into a Cox predictive model associated with overall survival in LUSC patients. Methylation and gene expression combined survival analysis showed that the survival rate of hypermethylation and low-expression of DQX1 and WDR61 were low. The expression of DQX1 had a significantly negatively correlated with the methylation site cg02034222. Conclusion: Methylation-driven genes DQX1 and WDR61 might be potential biomarkers for predicting the prognosis of LUSC.

Hypermethylation of N-Acetyltransferase 1 Is a Prognostic Biomarker in Colon Adenocarcinoma

Background: The N-acetyltransferase 1 (NAT1) gene is downregulated in several cancers and associated with patient survival. In this study, we sought to examine the prognostic value and clinical significance of NAT1 methylation in colon adenocarcinoma (COAD).

Methods: Data relating to NAT1 mRNA expression and methylation and clinicopathological features of COAD were extracted from the database of The Cancer Genome Atlas. We compared the mRNA expression and methylation of NAT1 between COAD and normal tissues and performed correlation analysis to assess the association between NAT1 mRNA expression and methylation. Furthermore, we assessed patient survival based on CpG sites in the promoter region of NAT1 and analyzed the association between the NAT1 mRNA expression and CpG site methylation and clinicopathological features. An independent Gene Expression Omnibus (GEO) dataset was used to validate the results.

Results: We found that the expression of NAT1 mRNA was reduced in COAD compared with normal tissues and that mean methylation of the eight CpG sites in the promoter region of NAT1 was higher in COAD tissues than in normal tissues. Furthermore, five CpG sites were demonstrated to be significantly negatively correlated with NAT1 mRNA expression in COAD. Survival analysis indicated that NAT1 mRNA expression and the cg15797286 and cg18509990 sites were associated with the overall survival of COAD patients. Combined survival analysis revealed that combinations of NAT1 mRNA expression with five CpG sites were significantly associated with the overall survival of COAD patients. Both NAT1 mRNA and cg15797286 were associated with the T, N, and clinical stages of COAD. The GEO data indicated that cg15797286 was hypermethylated in recurrent colorectal adenomas.

Conclusions: Methylation of NAT1 is associated with the development of COAD, and may serve as prognostic and treatment biomarkers for COAD.

Integrated analysis highlights multiple long non‑coding RNAs and their potential roles in the progression of human esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is a prevalent aggressive malignant tumor with poor prognosis. Investigations into the molecular changes that occur as a result of the disease, as well as identification of novel biomarkers for its diagnosis and prognosis, are urgently required. Long non‑coding RNAs (lncRNAs) have been reported to play a critical role in tumor progression. The present study performed data mining analyses for ESCC via an integrated study of accumulated datasets and identification of the differentially expressed lncRNAs from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. The identified intersection of differentially expressed genes (lncRNAs, miRNAs and mRNAs) in ESCC tissues between the GEO and TCGA datasets was investigated. Based on these intersected lncRNAs, the present study constructed a competitive endogenous RNA (ceRNA) network of lncRNAs in ESCC. A total of 81 intersection lncRNAs were identified; 67 of these were included in the ceRNA network. Functional analyses revealed that these 67 key lncRNAs primarily dominated cellular biological processes. The present study then analyzed the associations between the expression levels of these 67 key lncRNAs and the clinicopathological characteristics of the ESCC patients, as well as their survival time using TCGA. The results revealed that 31 of these lncRNAs were associated with tumor grade, tumor‑node‑metastasis (TNM) stage and lymphatic metastasis status (P<0.05). In addition, 15 key lncRNAs were demonstrated to be associated with survival time (P<0.05). Finally, 5 key lncRNAs were selected for validation of their expression levels in 30 patients newly diagnosed with ESCC via reverse transcription‑quantitative PCR (RT‑qPCR). The results suggested that the fold changes in the trends of up‑ and downregulation between GEO, TCGA and RT‑qPCR were consistent. In addition, it was also demonstrated that a select few of these 5 key lncRNAs were significantly associated with TNM stage and lymph node metastasis (P<0.05). The results of the clinically relevant analysis and the aforementioned bioinformatics were similar, hence proving that the bioinformatics analysis used in the present study is credible. Overall, the results from the present study may provide further insight into the functional characteristics of lncRNAs in ESCC through bioinformatics integrative analysis of the GEO and TCGA datasets, and reveal potential diagnostic and prognostic biomarkers for ESCC.

Methylation and transcriptome analysis reveal lung adenocarcinoma-specific diagnostic biomarkers

Background

DNA methylation can regulate the role of long noncoding RNAs (lncRNAs) in the development of lung adenocarcinoma (LUAD). The present study aimed to identify methylation-driven lncRNAs and mRNAs as biomarkers in the prognosis of LUAD using bioinformatics analysis.

Methods

Differentially expressed RNAs were obtained using the edge R package from 535 LUAD tissues and 59 adjacent non-LUAD tissues. Differentially methylated genes were obtained using the limma R package from 475 LUAD tissues and 32 adjacent non-LUAD tissues. Methylation-driven mRNA and lncRNA were obtained using the MethylMix R package from 465 LUAD tissues with matched DNA methylation and RNA expression and 32 non-LUAD tissues with DNA methylation. Gene ontology and ConsensusPathDB pathway analysis were performed to identify functional enrichment of methylation-driven mRNAs. Univariate and multivariate Cox regression analyses were performed to identify the independent effect of each variable for predicting the prognosis of LUAD. Kaplan–Meier curve analysis of DNA methylation and gene expression might provide potential prognostic biomarkers for LUAD patients.

Results

A total of 99 methylation-driven mRNAs and 17 methylation-driven lncRNAs were obtained. Univariate and multivariate Cox regression analysis showed that 6 lncRNAs (FOXE1, HOXB13-AS1_2, VMO1, HIST1H3F, AJ003147.8, ASXL3) were retrieved to construct a predictive model associated with overall survival in LUAD patients. Combined DNA methylation and gene expression survival analysis revealed that 4 lncRNAs (AC023824.1, AF186192.1, LINC01354 and WASIR2) and 8 mRNAs (S1PR1, CCDC181, F2RL1, EFS, KLHDC9, MPV17L, GKN2, ITPRIPL1) might act as independent biomarkers for the prognosis of LUAD.

Conclusions

Methylation-driven lncRNA and mRNA contribute to the survival of LUAD, and 4 lncRNAs and 8 mRNAs might be potential biomarkers for the prognosis of LUAD.

A six-gene prognostic model predicts overall survival in bladder cancer patients

Background

The fatality and recurrence rates of bladder cancer (BC) have progressively increased. DNA methylation is an influential regulator associated with gene transcription in the pathogenesis of BC. We describe a comprehensive epigenetic study performed to analyse DNA methylation-driven genes in BC.

Methods

Data related to DNA methylation, the gene transcriptome and survival in BC were downloaded from The Cancer Genome Atlas (TCGA). MethylMix was used to detect BC-specific hyper-/hypo-methylated genes. Metascape was used to carry out gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. A least absolute shrinkage and selection operator (LASSO)-penalized Cox regression was conducted to identify the characteristic dimension decrease and distinguish prognosis-related methylation-driven genes. Subsequently, we developed a six-gene risk evaluation model and a novel prognosis-related nomogram to predict overall survival (OS). A survival analysis was carried out to explore the individual prognostic significance of the six genes.

Results

In total, 167 methylation-driven genes were identified. Based on the LASSO Cox regression, six genes, i.e., ARHGDIB, LINC00526, IDH2, ARL14, GSTM2, and LURAP1, were selected for the development of a risk evaluation model. The Kaplan–Meier curve indicated that patients in the low-risk group had considerably better OS (P = 1.679e−05). The area under the curve (AUC) of this model was 0.698 at 3 years of OS. The verification performed in subgroups demonstrated the validity of the model. Then, we designed an OS-associated nomogram that included the risk score and clinical factors. The concordance index of the nomogram was 0.694. The methylation levels of IDH2 and ARL14 were appreciably related to the survival results. In addition, the methylation and gene expression-matched survival analysis revealed that ARHGDIB and ARL14 could be used as independent prognostic indicators. Among the six genes, 6 methylation sites in ARHGDIB, 3 in GSTM2, 1 in ARL14, 2 in LINC00526 and 2 in LURAP1 were meaningfully associated with BC prognosis. In addition, several abnormal methylated sites were identified as linked to gene expression.

Conclusion

We discovered differential methylation in BC patients with better and worse survival and provided a risk evaluation model by merging six gene markers with clinical characteristics.