Analysis of methylation‐driven genes for predicting the prognosis of patients with head and neck squamous cell carcinoma

Integrated immunogenomic analyses of single-cell and bulk profiling construct a T cell-related signature for predicting prognosis and treatment response in osteosarcoma

PURPOSES

T cells play a crucial role as regulators of anti-tumor activity within the tumor microenvironment (TME) and are closely associated with the progression of osteosarcoma (OS). Nevertheless, the specific role of T cell-related genes (TCRGs) in the pathogenesis of OS remains unclear.

METHODS

First, we processed single-cell RNA sequencing (scRNA-seq) data of OS from the public databases and performed cell annotation. We identified highly variable genes in each cell type using the "FindAllMarkers" function, explored the distribution of different clusters, and investigated inter-cellular communication patterns via the "CellChat" framework. Then, we used multivariate Cox analysis to construct a TCRG and developed a nomogram to predict survival probabilities for OS patients. Finally, we validated the aforementioned results using various cell lines and investigated the immune cell infiltration, expression of immune checkpoints, chemotherapy sensitivity, and the efficacy of targeted therapies across different risk groups.

RESULTS

From the scRNA-seq data, we identified 3,000 highly variable genes, presented the top 10 genes, and validated the expression of core genes across different cell lines.Moreover, our analysis delved into interactions between T cells and other cell types. Our analyses constructed a predictive T cell-related signature (TCRS) that incorporated these prognostic TCRGs, showing a clear prognostic separation between the high-risk and low-risk OS patient groups in multiple cohorts. Survival analysis indicated better outcomes for patients classified in the high-risk group. The low-risk group exhibited elevated levels of CD4 memory resting T cells, contrasting with the higher levels of macrophage M0 observed in the high-risk group via the CIBERSORT algorithm. Furthermore, we observed that the low-risk group exhibitedAQ1 significant up-regulation of immune checkpoint genes (ICGs) and lower Tumour Immune Dysfunction and Exclusion (TIDE) scores, suggesting that they may be suitable for immunotherapy. Conversely, the high-risk group appeared more responsive to chemotherapy and targeted therapies, according to our drug sensitivity analysis.

CONCLUSION

In conclusion, our study identified TCRGs, constructed and validated a TCRS for OS, and assessed immune response and drug sensitivity in different risk groups of OS patients. These findings provide novel insights into personalized treatment strategies for OS, potentially guiding more effective therapeutic interventions.

Head and neck cancer: pathogenesis and targeted therapy

Head and neck cancer (HNC) is a highly aggressive type of tumor characterized by delayed diagnosis, recurrence, metastasis, relapse, and drug resistance. The occurrence of HNC were associated with smoking, alcohol abuse (or both), human papillomavirus infection, and complex genetic and epigenetic predisposition. Currently, surgery and radiotherapy are the standard treatments for most patients with early-stage HNC. For recurrent or metastatic (R/M) HNC, the first-line treatment is platinum-based chemotherapy combined with the antiepidermal growth factor receptor drug cetuximab, when resurgery and radiation therapy are not an option. However, curing HNC remains challenging, especially in cases with metastasis. In this review, we summarize the pathogenesis of HNC, including genetic and epigenetic changes, abnormal signaling pathways, and immune regulation mechanisms, along with all potential therapeutic strategies such as molecular targeted therapy, immunotherapy, gene therapy, epigenetic modifications, and combination therapies. Recent preclinical and clinical studies that may offer therapeutic strategies for future research on HNC are also discussed. Additionally, new targets and treatment methods, including antibody-drug conjugates, photodynamic therapy, radionuclide therapy, and mRNA vaccines, have shown promising results in clinical trials, offering new prospects for the treatment of HNC.

Methylation related genes are associated with prognosis of patients with head and neck squamous cell carcinoma via altering tumor immune microenvironment

Background/purpose

Analysis of methylomes may enable prognostic stratification in patients with head and neck squamous cell carcinoma (HNSCC). This study aimed to identify methylation-related differentially expressed genes (mrDEGs), and to assess their efficacy in predicting patients' survival, tumor immune microenvironment alterations and immune checkpoints in patients with HNSCC.

Materials and methods

The methylome and transcriptome data of 528 HNSCC and 50 normal samples from TCGA database were used as training cohort. We identified mrDEGs and constituted a risk score model using Kaplan-Meier analysis and multivariate Cox regression. The prognostic efficacy of the risk score was validated in GSE65858 and GSE41613. We determined the enrichment of previously defined biological processes of mrDEGs. We separated the HNSCC patients into low-risk and high-risk groups and compared their immune cell infiltration and immune checkpoints' expressions.

Results

The risk score model was constituted by nine prognostic mrDEGs, including LIMD2, SYCP2, EPHX3, UCLH1, STC2, PRAME, SLC7A4, PLOD2, and ACADL. The risk score was a significant prognostic factor both in training (P < 0.001) and validation dataset (GSE65858: P = 0.008; GSE41613 = 0.015). The prognostic mrDEGs were enriched in multiple immune-associated pathways. Effector immune cells were increased in low-risk patients, including CD8+ T cells, activated CD4+ T cells, and plasma cells, whereas tumor associated M2 macrophages were recruited in the high-risk group. Expressions of immune checkpoints were generally higher in low-risk patients, including CTLA-4, PD-1 and LAG3.

Conclusion

The mrDEGs can stratify HNSCC patients' prognosis, which correlates with alterations in tumor immune infiltrations and immune checkpoints.

Evolving landscape of PD-L2: bring new light to checkpoint immunotherapy

Immune checkpoint blockade therapy targeting programmed cell death protein 1 (PD-1) has revolutionized the landscape of multiple human cancer types, including head and neck squamous carcinoma (HNSCC). Programmed death ligand-2 (PD-L2), a PD-1 ligand, mediates cancer cell immune escape (or tolerance independent of PD-L1) and predicts poor prognosis of patients with HNSCC. Therefore, an in-depth understanding of the regulatory process of PD-L2 expression may stratify patients with HNSCC to benefit from anti-PD-1 immunotherapy. In this review, we summarised the PD-L2 expression and its immune-dependent and independent functions in HNSCC and other solid tumours. We focused on recent findings on the mechanisms that regulate PD-L2 at the genomic, transcriptional, post-transcriptional, translational, and post-translational levels, also in intercellular communication of tumour microenvironment (TME). We also discussed the prospects of using small molecular agents indirectly targeting PD-L2 in cancer therapy. These findings may provide a notable avenue in developing novel and effective PD-L2-targeted therapeutic strategies for immune combination therapy and uncovering biomarkers that improve the clinical efficacy of anti-PD-1 therapies.

A methylation-driven genes prognostic signature and the immune microenvironment in epithelial ovarian cancer

Aberrant gene methylation has been implicated in the development and progression of tumors. In this study, we aimed to identity methylation driven genes involved in epithelial ovarian cancer (EOC) to establish a prognostic signature for patients with EOC. We identified and verified 6 MDGs that are closely related to the prognosis of ovarian cancer. A prognostic risk score model and nomogram for predicting the prognosis of ovarian cancer were constructed based on the six MDGs. It can also effectively reflect the immune environment and immunotherapy response of ovarian cancer. These MDGs have great significance to the implementation of individualized treatment and disease monitoring of ovarian cancer patients.

Multimetric feature selection for analyzing multicategory outcomes of colorectal cancer: random forest and multinomial logistic regression models

Colorectal cancer (CRC) is one of the most common cancers worldwide, and a leading cause of cancer deaths. Better classifying multicategory outcomes of CRC with clinical and omic data may help adjust treatment regimens based on individual's risk. Here, we selected the features that were useful for classifying four-category survival outcome of CRC using the clinical and transcriptomic data, or clinical, transcriptomic, microsatellite instability and selected oncogenic-driver data (all data) of TCGA. We also optimized multimetric feature selection to develop the best multinomial logistic regression (MLR) and random forest (RF) models that had the highest accuracy, precision, recall and F1 score, respectively. We identified 2073 differentially expressed genes of the TCGA RNASeq dataset. MLR overall outperformed RF in the multimetric feature selection. In both RF and MLR models, precision, recall and F1 score increased as the feature number increased and peaked at the feature number of 600-1000, while the models' accuracy remained stable. The best model was the MLR one with 825 features based on sum of squared coefficients using all data, and attained the best accuracy of 0.855, F1 of 0.738 and precision of 0.832, which were higher than those using clinical and transcriptomic data. The top-ranked features in the MLR model of the best performance using clinical and transcriptomic data were different from those using all data. However, pathologic staging, HBS1L, TSPYL4, and TP53TG3B were the overlapping top-20 ranked features in the best models using clinical and transcriptomic, or all data. Thus, we developed a multimetric feature-selection based MLR model that outperformed RF models in classifying four-category outcome of CRC patients. Interestingly, adding microsatellite instability and oncogenic-driver data to clinical and transcriptomic data improved models' performances. Precision and recall of tuned algorithms may change significantly as the feature number changes, but accuracy appears not sensitive to these changes.

Development of Potential Prognostic Biomarkers Based on DNA Methylation-Driven Genes for Patients with Endometrial Cancer

Background

Endometrial cancer (EC) is a multifactorial disease, and its progression may be driven by abnormal genetic methylation. To clarify the underlying molecular mechanisms and sensitive biomarkers for EC, this study used an integrated bioinformatic analysis to explore the methylation-driven genes of EC.

Methods

The mRNA expression data, methylation data and corresponding clinical information of EC samples were downloaded from The Cancer Genome Atlas (TCGA) database. MethylMix algorithm was used to screen out methylation-driven genes in EC. Functional and pathway enrichment analysis and the protein–protein interaction (PPI) analysis were conducted to demonstrate the functions and interactions between these genes. Then, prognosis-related methylated genes were screened out by using univariate and multivariate Cox analyses, and a prognostic risk assessment model for EC was constructed. The methylation sites and expression profiles of candidate genes were further investigated.

Results

A total of 127 methylated genes were identified in EC. Four genes (RP11-968O1.5, DCAF12L1, MSX1 and ALS2CR11) were selected as candidate genes to construct a reliable prognostic risk model. The univariate and multivariate Cox proportional hazards regression analyses showed that the risk score based on four genes was an independent prognostic indicator for OS among EC patients. A nomogram was established and the calibration plot analysis indicated the good performance and clinical utility of the nomogram. In addition, the methylation and expression of MSX1 and DCAF12L1 were significantly associated with EC survival rate. The joint ROC analysis revealed that the AUC of DCAF12L1-MSX1 was 0.867, which suggested both have a good EC-diagnosing efficiency. We then coped DCAF12L1 and MSX1 with GESA analysis, finding both were mainly associated with the KRAS signaling pathway.

Conclusion

This bioinformatic study combs the methylated genes involved in EC development for the first time, finding that MSX1 and DCAF12L1 could serve as EC prognostic markers and drug targets.

Analysis of Methylation-driven Genes in Pancreatic Ductal Adenocarcinoma for Predicting Prognosis

Purpose: Considerable variations in methylation profile have been found in various cancers to modulate tumorigenesis and affect prognosis. To provide a theoretical basis for early detection, prognosis evaluation and targeted treatment for patients with pancreatic ductal adenocarcinoma: PDAC, this study identified methylation-driven genes in PDAC and explored their prognostic performance.

Methods: The methylation, expression and clinical data of PDAC patients were extracted from TCGA database. Based on the β-mixture model of the MethylMix R package, the differential methylation status and connection between methylation and expression degree were examined to screen out methylation-driven genes in PDAC. COX analyses and lasso regressions were applied to construct a linear risk model based on methylation-driven genes. Univariate and multivariate analyses were performed to ensure the risk model was an independent prognostic factor. Joint survival analyses of methylation and gene expression were conducted to explore the prognostic value of component genes. The methylation sites in the key genes were also investigated.

Results: A total of 118 methylation-driven genes in PDAC were identified, and two genes (FOXI2, MYEOV) constituted the risk model whose AUC was 0.722 at one year of overall survival rate, displaying a better performance on survival prediction than other clinical features. Further survival analyses demonstrated that the expression of MYEOV and combined methylation and expression levels of the genes MYEOV and FOXI2 can be potential biomarkers for survival prediction and targets of drug manipulation of PDAC patients. Close relationships were discovered between two sites in MYEOV and one site in FOXI2 and the prognosis of PDAC patients.

Conclusion: Concentrating on DNA methylation, our study identified potential biomarkers and developed a reliable short-term predictive model for prognosis of PDAC patients.

Development and Validation of a Prognostic Nomogram Based on DNA Methylation-Driven Genes for Patients With Ovarian Cancer

Background: DNA methylation affects the development, progression, and prognosis of various cancers. This study aimed to identify DNA methylated-differentially expressed genes (DEGs) and develop a methylation-driven gene model to evaluate the prognosis of ovarian cancer (OC).

Methods: DNA methylation and mRNA expression profiles of OC patients were downloaded from The Cancer Genome Atlas, Genotype-Tissue Expression, and Gene Expression Omnibus databases. We used the R package MethylMix to identify DNA methylation-regulated DEGs and built a prognostic signature using LASSO Cox regression. A quantitative nomogram was then drawn based on the risk score and clinicopathological features.

Results: We identified 56 methylation-related DEGs and constructed a prognostic risk signature with four genes according to the LASSO Cox regression algorithm. A higher risk score not only predicted poor prognosis, but also was an independent poor prognostic indicator, which was validated by receiver operating characteristic (ROC) curves and the validation cohort. A nomogram consisting of the risk score, age, FIGO stage, and tumor status was generated to predict 3- and 5-year overall survival (OS) in the training cohort. The joint survival analysis of DNA methylation and mRNA expression demonstrated that the two genes may serve as independent prognostic biomarkers for OS in OC.

Conclusion: The established qualitative risk score model was found to be robust for evaluating individualized prognosis of OC and in guiding therapy.

An aberrant DNA methylation signature for predicting the prognosis of head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) is a common malignancy worldwide with a poor prognosis. DNA methylation is an epigenetic modification that plays a critical role in the etiology and pathogenesis of HNSCC. The current study aimed to develop a predictive methylation signature based on bioinformatics analysis to improve the prognosis and optimize therapeutic outcome in HNSCC. Clinical information and methylation sequencing data of patients with HNSCC were downloaded from The Cancer Genome Atlas database. The R package was used to identify differentially methylated genes (DMGs) between HNSCC and adjacent normal tissues. We identified 22 DMGs associated with 246 differentially methylated sites. Patients with HNSCC were classified into training and test groups. Cox regression analysis was used to build a risk score formula based on the five methylation sites (cg26428455, cg13754259, cg17421709, cg19229344, and cg11668749) in the training group. The Kaplan–Meier survival curves showed that the overall survival (OS) rates were significantly different between the high‐ and low‐risk groups sorted by the signature in the training group (median: 1.38 vs. 1.57 years, log‐rank test, p < 0.001). The predictive power was then validated in the test group (median: 1.34 vs. 1.75 years, log‐rank test, p < 0.001). The area under the receiver operating characteristic curve (area under the curve) based on the signature for predicting the 5‐year survival rates, was 0.7 in the training and 0.73 in test groups, respectively. The results of multivariate Cox regression analysis showed that the riskscore (RS) signature based on the five methylation sites was an independent prognostic tool for OS prediction in patients. In addition, a predictive nomogram model that incorporated the RS signature and patient clinical information was developed. The innovative methylation signature‐based model developed in our study represents a robust prognostic tool for guiding clinical therapy and predicting the OS in patients with HNSCC.

HPV Infection Leaves a DNA Methylation Signature in Oropharyngeal Cancer Affecting Both Coding Genes and Transposable Elements

HPV oncoproteins can modulate DNMT1 expression and activity, and previous studies have reported both gene-specific and global DNA methylation alterations according to HPV status in head and neck cancer. However, validation of these findings and a more detailed analysis of the transposable elements (TEs) are still missing. Here we performed pyrosequencing to evaluate a 5-CpG methylation signature and Line1 methylation in an oropharyngeal squamous cell carcinoma (OPSCC) cohort. We further evaluated the methylation levels of the TEs, their correlation with gene expression and their impact on overall survival (OS) using the TCGA cohort. In our dataset, the 5-CpG signature distinguished HPV-positive and HPV-negative OPSCC with 66.67% sensitivity and 84.33% specificity. Line1 methylation levels were higher in HPV-positive cases. In the TCGA cohort, Line1, Alu and long terminal repeats (LTRs) showed hypermethylation in a frequency of 60.5%, 58.9% and 92.3%, respectively. ZNF541 and CCNL1 higher expression was observed in HPV-positive OPSCC, correlated with lower methylation levels of promoter-associated Alu and LTR, respectively, and independently associated with better OS. Based on our findings, we may conclude that a 5-CpG methylation signature can discriminate OPSCC according to HPV status with high accuracy and TEs are differentially methylated and may regulate gene expression in HPV-positive OPSCC.

Identification of methylation-driven genes prognosis signature and immune microenvironment in uterus corpus endometrial cancer

Background

Uterus corpus endometrial cancer (UCEC) is the main malignant tumor in gynecology, with a high degree of heterogeneity, especially in terms of prognosis and immunotherapy efficacy. DNA methylation is one of the most important epigenetic modifications. Studying DNA methylation can help predict the prognosis of cancer patients and provide help for clinical treatment. Our research aims to discover whether abnormal DNA methylation can predict the prognosis of UCEC and reflect the patient's tumor immune microenvironment.

Patients and methods

The clinical data, DNA methylation data, gene expression data and somatic mutation data of UCEC patients were all downloaded from the TCGA database. The MethylMix algorithm was used to integrate DNA methylation data and mRNA expression data. Univariate Cox regression analysis, Multivariate Cox regression analysis, and Lasso Cox regression analysis were used to determine prognostic DNA methylation-driven genes and to construct an independent prognostic index (MDS). ROC curve analysis and Kaplan–Meier survival curve analysis were used to evaluate the predictive ability of MDS. GSEA analysis was used to explore possible mechanisms that contribute to the heterogeneity of the prognosis of UCEC patients.

Results

3 differential methylation-driven genes (DMDGs) (PARVG, SYNE4 and CDO1) were considered as predictors of poor prognosis in UCEC. An independent prognostic index was finally established based on 3 DMDGs. From the results of ROC curve analysis and survival curve analysis, MDS showed excellent prognostic ability in TCGA-UCEC. A new nomogram based on MDS and other prognostic clinical indicators has also been successfully established. The C-index of the nomogram for OS prediction was 0.764 (95% CI = 0.702–0.826). GSEA analysis suggests that there were differences in immune-related pathways among patients with different prognosis. The abundance of M2 macrophages and M0 macrophages were significantly enhanced in the high-risk group while T cells CD8, Eosinophils and Neutrophils were markedly elevated in the low-risk group. Meanwhile, patients in the low-risk group had higher levels of immunosuppressant expression, higher tumor mutational burden and immunophenoscore (IPS) scores. Joint survival analysis revealed that 7 methylation-driven genes could be independent prognostic factors for overall survival for UCEC.

Conclusion

We have successfully established a risk model based on 3 DMDGs, which could accurately predict the prognosis of patients with UCEC and reflect the tumor immune microenvironment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02038-z.

α/β-Hydrolase Domain (ABHD) Inhibitors as New Potential Therapeutic Options against Lipid-Related Diseases

Much of the experimental evidence in the literature has linked altered lipid metabolism to severe diseases such as cancer, obesity, cardiovascular pathologies, diabetes, and neurodegenerative diseases. Therefore, targeting key effectors of the dysregulated lipid metabolism may represent an effective strategy to counteract these pathological conditions. In this context, α/β-hydrolase domain (ABHD) enzymes represent an important and diversified family of proteins, which are involved in the complex environment of lipid signaling, metabolism, and regulation. Moreover, some members of the ABHD family play an important role in the endocannabinoid system, being designated to terminate the signaling of the key endocannabinoid regulator 2-arachidonoylglycerol. This Perspective summarizes the research progress in the development of ABHD inhibitors and modulators: design strategies, structure-activity relationships, action mechanisms, and biological studies of the main ABHD ligands will be highlighted.

A methylation-driven gene panel predicts survival in patients with colon cancer

The accumulation of various genetic and epigenetic changes in colonic epithelial cells has been identified as one of the fundamental processes that drive the initiation and progression of colorectal cancer (CRC). This study aimed to explore functional genes regulated by DNA methylation and their potential utilization as biomarkers for the prediction of CRC prognoses. Methylation‐driven genes (MDGs) were explored by applying the integrative analysis tool (methylmix) to The Cancer Genome Atlas CRC project. The prognostic MDG panel was identified by combining the Cox regression model with the least absolute shrinkage and selection operator regularization. Gene set enrichment analysis was used to determine the pathways associated with the six‐MDG panel. Cluster of differentiation 40 (CD40) expression and methylation in CRC samples were validated by using additional datasets from the Gene Expression Omnibus. Methylation‐specific PCR and bisulfite sequencing were used to confirm DNA methylation in CRC cell lines. A prognostic MDG panel consisting of six gene members was identified: TMEM88, HOXB2, FGD1, TOGARAM1, ARHGDIB and CD40. The high‐risk phenotype classified by the six‐MDG panel was associated with cancer‐related biological processes, including invasion and metastasis, angiogenesis and the tumor immune microenvironment. The prognostic value of the six‐MDG panel was found to be independent of tumor node metastasis stage and, in combination with tumor node metastasis stage and age, could help improve survival prediction. In addition, the expression of CD40 was confirmed to be regulated by promoter region methylation in CRC samples and cell lines. The proposed six‐MDG panel represents a promising signature for estimating the prognosis of patients with CRC.

Development and clinical validation of a novel 9-gene prognostic model based on multi-omics in pancreatic adenocarcinoma

The prognoses of patients with pancreatic adenocarcinoma (PAAD) remain poor due to the lack of biomarkers for early diagnosis and effective prognosis prediction. RNA sequencing, single nucleotide polymorphism, and copy number variation data were downloaded from The Cancer Genome Atlas (TCGA). Univariate Cox regression was used to identify prognosis-related genes. GISTIC 2.0 was used to identify significantly amplified or deleted genes, and Mutsig 2.0 was used to analyze the mutation data. The Lasso method was used to construct a risk prediction model. The Rms package was used to evaluate the overall predictive performance of the signature. Finally, Western blot and polymerase chain reaction were performed to evaluate gene expression. A total of 54 candidate genes were obtained after integrating the genomic mutated genes and prognosis-related genes. The Lasso method was used to ascertain 9 characteristic genes, including UNC13B, TSPYL4, MICAL1, KLHDC7B, KLHL32, AIM1, ARHGAP18, DCBLD1, and CACNA2D4. The 9-gene signature model was able to help stratify samples at risk in the training and external validation cohorts. In addition, the overall predictive performance of our model was found to be superior to that of other models. KLHDC7B, AIM1, DCBLD1, TSPYL4, and MICAL1 were significantly highly expressed in tumor tissues compared to normal tissues. ARHGAP18 and CACNA2D4 had no difference in expression between tumor and normal tissues. UNC13B and KLHL32 expression in the normal group was higher than in the tumor group. The 9-gene signature constructed in this study can be used as a novel prognostic marker to predict the survival of patients with pancreatic adenocarcinoma.

Novel Prognostic Model Based on Immune Signature for Head and Neck Squamous Cell Carcinoma

Background

Deciphering the immune characteristics within tumors and identifying the immune signals related to the prognostic factor are helpful for the treatment and management of tumor patients. However, systematic analysis of immune signatures in head and neck squamous cell carcinoma (HNSCC) remains largely unstudied.

Methods

A total of 718 immune-related genes were extracted from RNA sequencing data from 519 HNSCC patients in the TCGA database, and survival analysis with integrated bioinformatics analyses was performed to build the final predictive prognosis model.

Results

The 178 survival-associated genes (P < 0.05) participated in important immune functions, including immune cell activation and migration. Multivariate regression analysis using 93 genes (P < 0.01), together with survival-associated clinicopathological parameters, identified 35 independent prognostic factors. The most significant 8 independent factors were CD3E, CD40LG, TNFRSF4, CD3G, CD5, ITGA2B, ABCB1, and TNFRSF13b. The final prognostic model achieved outstanding predictive efficiency with the highest AUC of 0.963.

Conclusion

Our prognostic model based on the immune signature could effectively predict the prognosis of HNSCC patients, providing novel predictive biomarkers and potential therapeutic targets for HNSCC patients.

Pre-Operative Evaluation of DNA Methylation Profile in Oral Squamous Cell Carcinoma Can Predict Tumor Aggressive Potential

BACKGROUND

Prognosis of oral squamous cell carcinoma (OSCC) is difficult to exactly assess on pre-operative biopsies. Since OSCC DNA methylation profile has proved to be a useful pre-operative diagnostic tool, the aim of the present study was to evaluate the prognostic impact of DNA methylation profile to discriminate OSCC with high and low aggressive potential.

METHODS

36 OSCC cases underwent neoplastic cells collection by gentle brushing of the lesion, before performing a pre-operative biopsy. The CpG islands methylation status of 13 gene (ZAP70, ITGA4, KIF1A, PARP15, EPHX3, NTM, LRRTM1, FLI1, MiR193, LINC00599, MiR296, TERT, GP1BB) was studied by bisulfite Next Generation Sequencing (NGS). A Cox proportional hazards model via likelihood-based component-wise boosting was used to evaluate the prognostic power of the CpG sites.

RESULTS

The boosting estimation identified five CpGs with prognostic significance: EPHX3-24, EPHX3-26, ITGA4-3, ITGA4-4, and MiR193-3. The combination of significant CpGs provided promising results for adverse events prediction (Brier score = 0.080, C-index = 0.802 and AUC = 0.850). ITGA4 had a strong prognostic power in patients with early OSCC.

CONCLUSIONS

These data confirm that the study of methylation profile provides new insights into the molecular mechanisms of OSCC and can allow a better OSCC prognostic stratification even before surgery.

Multiomic Analysis of Methylation and Transcriptome Reveals a Novel Signature in Esophageal Cancer

Background:

Epigenetic alterations have been shown to lead to human carcinogenesis. The aim of this study was to perform an integrative analysis to develop an epigenetic signature to predict overall survival (OS) of esophageal cancer.

Methods:

DNA methylation and messenger RNA expression data of esophageal cancer samples were downloaded from The Cancer Genome Atlas database and were incorporated and analyzed using an R package MethylMix. Functional enrichment analysis of the methylation-related differentially expressed genes (DEGs) was performed. Epigenetic signature and nomogram associated with the OS of esophageal cancer were established by the multivariate Cox model.

Results:

A total of 71 methylation-related DEGs were identified. Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed that these genes were involved in the biological process related to the initiation and progression of esophageal cancer. Two-gene (FAM24B and FAM200A) risk signature for OS was developed by multivariate Cox analysis, of which had high accuracy. The signature is independent of clinicopathological variables and indicated better predictive power than other clinicopathological variables. Moreover, we developed a novel prognostic nomogram based on risk score and 3 clinicopathological factors.

Conclusions:

Our study indicated possible methylation-related DEGs and established an epigenetic signature, which may provide novel insights for understanding the pathogenesis of esophageal cancer.

A novel epigenetic signature to predict recurrence-free survival in patients with colon cancer

BACKGROUND

DNA methylation plays an important role in the initiation and progression of colon cancer. The aim of the present study was to perform a comprehensive analysis of DNA methylation and gene expression profiles in order to develop a signature to predict recurrence-free survival (RFS) of colon cancer.

METHODS

DNA methylation and mRNA expression data were obtained from TCGA database, and were analyzed using an R package MethylMix. Functional enrichment analysis was performed on statistically significant genes identified by MethylMix criteria. The epigenetic signature and nomogram associated with the RFS of colon cancer were established by the Least Absolute Shrinkage and Selection Operator (LASSO) Cox model. Additionally, a joint survival analysis of gene expression and methylation was performed to identify potential prognostic factors for patients with colon cancer.

RESULTS

A total of 179 differentially methylated genes were obtained using MethylMix algorithm. An epigenetic signature for RFS was developed using LASSO. Patients with high-risk had significantly worse RFS than those with low-risk. The signature is independent of clinicopathological variables and indicated better predictive power than other clinicopathological variables in patients with colon cancer. Moreover, joint survival analysis of gene expression and methylation revealed that seven methylated genes could be independent prognostic factors for RFS in colon cancer.

CONCLUSIONS

Our proposed epigenetic signature presents potential prognostic significance in assessing recurrence risk stratification for patients with colon cancer.

Genome-wide methylation and expression profiling identify a novel epigenetic signature in gastrointestinal pan-adenocarcinomas

Aim: To identify methylation-driven genes and establish a novel epigenetic signature for gastrointestinal (GI) pan-adenocarcinomas. Materials & methods: Methylation and RNA-seq data for GI adenocarcinomas were downloaded from the Cancer Genome Atlas database. A methylation-driven gene signature was established by multivariate Cox regression analysis. We developed a prognostic nomogram using a combination of methylation-driven gene risk score and clinicopathological variables. A joint survival analysis based on gene expression and methylation was conducted to further investigate the prognostic role of methylation-driven genes. Results: An epigenetic signature was established based on five methylation-driven genes. We also established a prognostic nomogram based on methylation-driven gene risk score and clinicopathologic factors, with a favorable predictive ability. Joint survival analysis revealed that 28 methylation-driven genes could be independent prognostic factors for overall survival for GI adenocarcinomas. Conclusion: An epigenetic signature was established that effectively predicts the overall survival for GI adenocarcinomas across anatomic boundaries.

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.

The relevance between the immune response-related gene module and clinical traits in head and neck squamous cell carcinoma

Purpose

Head and neck squamous cell carcinoma (HNSCC) is the sixth most prevalent cancer in the world, accounting for more than 90% of head and neck malignant tumors. However, its molecular mechanism is largely unknown. To help elucidate the potential mechanism of HNSCC tumorigenesis, we investigated the gene interaction patterns associated with tumorigenesis.

Methods

Weighted gene co-expression network analysis (WGCNA) can help us to predict the intrinsic relationship or correlation between gene expression. Additionally, we further explored the combination of clinical information and module construction.

Results

Sixteen modules were constructed, among which the key module most closely associated with clinical information was identified. By analyzing the genes in this module, we found that the latter may be related to the immune response, inflammatory response and formation of the tumor microenvironment. Sixteen hub genes were identified-ARHGAP9, SASH3, CORO1A, ITGAL, PPP1R16B, TBC1D10C, IL10RA, ITK, AKNA, PRKCB, TRAF3IP3, GIMAP4, CCR7, P2RY8, GIMAP7, and SP140. We further validated these genes at the transcriptional and translation levels.

Conclusion

The innovative use of a weighted network to analyze HNSCC samples provides new insights into the molecular mechanism and prognosis of HNSCC. Additionally, the hub genes we identified can be used as biomarkers and therapeutic targets of HNSCC, laying the foundation for the accurate diagnosis and treatment of HNSCC in clinical and research in the future.