Identification of hub methylated-CpG sites and associated genes in oral squamous cell carcinoma

OSR1 downregulation indicates an unfavorable prognosis and activates the NF-κB pathway in ovarian cancer

BACKGROUND

Odd-skipped related 1 (OSR1) has been reported as a tumor suppressor gene in various malignant tumors. The mechanism through which OSR1 regulates ovarian cancer (OC) progression remains unclear.

MATERIALS AND METHODS

Immunohistochemistry was utilized to evaluate OSR1 expression in patients with ovarian cancer. We investigated the association between clinicopathological parameters and OSR1 expression in OC patients and the influence of OSR1 expression on patient survival and prognosis. OC cells with OSR1 overexpression or knockdown were established and validated using Western blot and Quantitative reverse-transcription polymerase chain reaction (qRT-PCR). The influence of OSR1 on the NF-κB pathway was examined by analyzing the p-IκBα, IκBα, p65, and p-p65 protein expression. In vitro assays, such as cell cycle assay, Cell Counting Kit-8 (CCK-8), transwell invasion assay, wound healing migration assay, enzyme-linked immunoassay (ELISA), and Annexin V/PI flow cytometry apoptosis assay, were conducted to explore the effect of OSR1 knockdown or dual inhibition of OSR1 and the NF-κB pathway on OC malignant biological behavior.

RESULTS

OSR1 expression was downregulated in OC tissues, with significant associations observed between its expression and The International Federation of Gynecology and Obstetrics (FIGO) stage and tissue differentiation. Low OSR1 expression in OC patients correlated with reduced overall survival (OS) rates and poor prognosis. In vitro, experiments confirmed a negative correlation between OSR1 expression and NF-κB pathway activity. OSR1 knockdown facilitated OC cell malignant biological behavior, while the NF-κB pathway inhibitor (Bay 11-0782) reversed the impacts of OSR1 knockdown on cell proliferation, migration, invasion, and apoptosis.

CONCLUSION

Our findings indicate that OSR1 is downregulated and associated with OC prognosis. OSR1 suppresses NF-κB pathway activity and inhibits OC progression by targeting the NF-κB pathway.

Epigenetic methylation changes: implication as biomarkers in oral and maxillofacial area cancers

Background/Aim

Squamous cell carcinoma (SCC) is the most frequent cancer of the head and neck area in the oral cavity. Epigenetic alterations in oral and maxillofacial area cancers are urgently needed to be investigated, as the observed changes might have crucial diagnostic value for personalized medicine.

Methods

Our study aimed to identify the most frequently hypermethylated tumor suppressor gene promoters in OSCC, followed by correlation analysis with the patients' survival. We evaluated the methylation status of the promoters in a panel of 22 tumor suppressor genes in Romanian (n=9) and Bulgarian (n=12) patient groups suffering from oral and maxillofacial area cancers. The extracted DNA was further digested through EpiTect Methyl II PCR Array System containing methylation-sensitive and methylation-dependent restriction enzymes, followed by specific amplification of the products obtained by qPCR and data analysis using the online platform provided by the producer.

Results

Different methylation patterns were observed in the tumor suppressor genes' promoters. Among them, the methylation profile of Cccnd2, Chd1, Cdh13, Cdkn1c, Neurog1, Gstp1, and Runx3 genes further correlated with overall survival rates.

Conclusions

Our data emphasize that epigenetic alterations are responsible for the clinical heterogeneity of oral and maxillofacial area cancers and significantly impact on patient survival. Additional investigation on a larger patient cohort should validate these potential biomarkers.

ZNF677 inhibits oral squamous cell carcinoma growth and tumor stemness by regulating FOXO3a

Oral squamous cell carcinoma (OSCC) is a common cancer with an increasing incidence worldwide. Zinc-finger proteins 677 (ZNF677) is involved in the progression and methylation of various cancers, but its role and mechanism in OSCC remain indeterminate. The expression of ZNF677 was analyzed by online database and immunohistochemistry, while the methylation level of ZNF677 was determined by the methylation-specific PCR. The role and mechanism of ZNF677 in the tumor cell growth, migration, invasion and stemness were addressed by cell counting kit-8 (CCK-8), 5-ethynyl-2'-deoxyuridine (EdU) incorporation, Transwell, wound-healing, sphere‑formation, and western blot assays. In addition, its function was also investigated in a xenografted mice model. The results showed that ZNF677 was lowly expressed in OSCC with a hypermethylation level, which predicted poor overall survival in patients with HNSC. Upregulation of ZNF677 reduced the cell viability, Edu positive cells, numbers of invasion cells, the migration ability, numbers of spheres formation and the expression of proliferation, migration and stemness related proteins in CAL-27 and SCC25 cells. Mechanically, the relative levels of p-AKT/AKT were decreased and the levels of p-FOXO3a/FOXO3a were increased in both cells overexpressed with ZNF677, which were reversed by the SC79 treatment. Moreover, interference of FOXO3a recovered the suppressive effects of ZNF677 overexpression on cell proliferation, migration, invasion and stemness of OSCC cells. Furthermore, overexpression of ZNF677 reduced the tumor volume and weight, and the relative protein level of p-AKT/AKT with an increased level of p-FOXO3a/FOXO3a, and improved pathological symptoms in vivo. Collectively, ZNF677 suppressed OSCC cells growth, migration, invasion and stemness through inhibiting AKT/FOXO3a pathway.

Cancer subtyping with heterogeneous multi-omics data via hierarchical multi-kernel learning

Differentiating cancer subtypes is crucial to guide personalized treatment and improve the prognosis for patients. Integrating multi-omics data can offer a comprehensive landscape of cancer biological process and provide promising ways for cancer diagnosis and treatment. Taking the heterogeneity of different omics data types into account, we propose a hierarchical multi-kernel learning (hMKL) approach, a novel cancer molecular subtyping method to identify cancer subtypes by adopting a two-stage kernel learning strategy. In stage 1, we obtain a composite kernel borrowing the cancer integration via multi-kernel learning (CIMLR) idea by optimizing the kernel parameters for individual omics data type. In stage 2, we obtain a final fused kernel through a weighted linear combination of individual kernels learned from stage 1 using an unsupervised multiple kernel learning method. Based on the final fusion kernel, k-means clustering is applied to identify cancer subtypes. Simulation studies show that hMKL outperforms the one-stage CIMLR method when there is data heterogeneity. hMKL can estimate the number of clusters correctly, which is the key challenge in subtyping. Application to two real data sets shows that hMKL identified meaningful subtypes and key cancer-associated biomarkers. The proposed method provides a novel toolkit for heterogeneous multi-omics data integration and cancer subtypes identification.

MicroRNA-Based Markers of Oral Tongue Squamous Cell Carcinoma and Buccal Squamous Cell Carcinoma: A Systems Biology Approach

Objective

Oral tongue squamous cell carcinoma (OTSCC) and buccal squamous cell carcinoma (BSCC) are the first and second leading causes of oral cancer, respectively. OTSCC and BSCC are associated with poor prognosis in patients with oral cancer. Thus, we aimed to indicate signaling pathways, Gene Ontology terms, and prognostic markers mediating the malignant transformation of the normal oral tissue to OTSCC and BSCC.

Methods

The dataset GSE168227 was downloaded and reanalyzed from the GEO database. Orthogonal partial least square (OPLS) analysis identified common differentially expressed miRNAs (DEMs) in OTSCC and BSCC compared to their adjacent normal mucosa. Next, validated targets of DEMs were identified using the TarBase web server. With the use of the STRING database, a protein interaction map (PIM) was created. Using the Cytoscape program, hub genes and clusters within the PIM were shown. Next, gene-set enrichment analysis was carried out using the g:Profiler tool. Using the GEPIA2 web tool, analyses of gene expression and survival analysis were also performed.

Results

Two DEMs, including has-miR-136 and has-miR-377, were common in OTSCC and BSCC (p value <0.01; |Log2 FC| > 1). A total of 976 targets were indicated for common DEMs. PIM included 96 hubs, and the upregulation of EIF2S1, CAV1, RAN, ANXA5, CYCS, CFL1, MYC, HSP90AA1, PKM, and HSPA5 was significantly associated with a poor prognosis in the head and neck squamous cell carcinoma (HNSCC), while NTRK2, HNRNPH1, DDX17, and WDR82 overexpression was significantly linked to favorable prognosis in the patients with HNSCC. "Clathrin-mediated endocytosis" was considerably dysregulated in OTSCC and BSCC.

Conclusion

The present study suggests that has-miR-136 and has-miR-377 are underexpressed in OTSCC and BSCC than in normal oral mucosa. Moreover, EIF2S1, CAV1, RAN, ANXA5, CYCS, CFL1, MYC, HSP90AA1, PKM, HSPA5, NTRK2, HNRNPH1, DDX17, and WDR82 demonstrated prognostic markers in HNSCC. These findings may benefit the prognosis and management of individuals with OTSCC/BSCC. However, additional experimental verification is required.

Comethyl: a network-based methylome approach to investigate the multivariate nature of health and disease

Health outcomes are frequently shaped by difficult to dissect inter-relationships between biological, behavioral, social and environmental factors. DNA methylation patterns reflect such multivariate intersections, providing a rich source of novel biomarkers and insight into disease etiologies. Recent advances in whole-genome bisulfite sequencing enable investigation of DNA methylation over all genomic CpGs, but existing bioinformatic approaches lack accessible system-level tools. Here, we develop the R package Comethyl, for weighted gene correlation network analysis of user-defined genomic regions that generates modules of comethylated regions, which are then tested for correlations with multivariate sample traits. First, regions are defined by CpG genomic location or regulatory annotation and filtered based on CpG count, sequencing depth and variability. Next, correlation networks are used to find modules of interconnected nodes using methylation values within the selected regions. Each module containing multiple comethylated regions is reduced in complexity to a single eigennode value, which is then tested for correlations with experimental metadata. Comethyl has the ability to cover the noncoding regulatory regions of the genome with high relevance to interpretation of genome-wide association studies and integration with other types of epigenomic data. We demonstrate the utility of Comethyl on a dataset of male cord blood samples from newborns later diagnosed with autism spectrum disorder (ASD) versus typical development. Comethyl successfully identified an ASD-associated module containing regions mapped to genes enriched for brain glial functions. Comethyl is expected to be useful in uncovering the multivariate nature of health disparities for a variety of common disorders. Comethyl is available at github.com/cemordaunt/comethyl with complete documentation and example analyses.

Integrative Multi-Omics Analysis Reveals Candidate Biomarkers for Oral Squamous Cell Carcinoma

Oral squamous cell carcinoma (OSCC) is one of the most common types of cancer worldwide. Due to the lack of early detection and treatment, the survival rate of OSCC remains poor and the incidence of OSCC has not decreased during the past decades. To explore potential biomarkers and therapeutic targets for OSCC, we analyzed differentially expressed genes (DEGs) associated with OSCC using RNA sequencing technology. Methylation-regulated and differentially expressed genes (MeDEGs) of OSCC were further identified via an integrative approach by examining publicly available methylomic datasets together with our transcriptomic data. Protein-protein interaction (PPI) networks of MeDEGs were constructed and highly connected hub MeDEGs were identified from these PPI networks. Subsequently, expression and survival analyses of hub genes were performed using The Cancer Genome Atlas (TCGA) database and the Gene Expression Profiling Interactive Analysis (GEPIA) online tool. A total of 56 upregulated MeDEGs and 170 downregulated MeDEGs were identified in OSCC. Eleven hub genes with high degree of connectivity were picked out from the PPI networks constructed by those MeDEGs. Among them, the expression level of four hub genes (CTLA4, CDSN, ACTN2, and MYH11) were found to be significantly changed in the head and neck squamous carcinoma (HNSC) patients. Three hypomethylated hub genes (CTLA4, GPR29, and TNFSF11) and one hypermethylated hub gene (ISL1) were found to be significantly associated with overall survival (OS) of HNSC patients. Therefore, these hub genes may serve as potential DNA methylation biomarkers and therapeutic targets of OSCC.

Lysine 2-hydroxyisobutyrylation proteomics reveals protein modification alteration in the actin cytoskeleton pathway of oral squamous cell carcinoma

As the most commonplace malignant carcinoma in the oral cavity, oral squamous cell carcinoma (OSCC) is highly invasive and prone to recurrence. The nosogenesis of OSCC are affected by epigenetics. Recently, a newly-found post-translational modification of lysine, 2-hydroxyisobutylation (Khib), has been proved to play a critical role in biological regulation. However, no research has evaluated the mechanism of Khib in oral cancer. Here, we performed liquid chromatography-mass spectrometry-based quantitative proteomics combined with bioinformatics analysis to reveal and evaluate Khib protein alterations in OSCC. Numerous proteins in OSCC undergo up-regulated modification of Khib. We quantified and identified 967 proteins with differential expression levels, and 617 2-hydroxyisobutylated proteins with 938 Khib sites. Among them, 125 proteins both differentially expressed and accompanied by obvious Khib modification were further identified and analyzed through KEGG-based and ingenuity pathway analysis (IPA). These proteins are enriched in the actin cytoskeleton regulatory pathway, and IPA predicted that they alter the state of actin aggregation and stability, hence impacting and regulating the actin cytoskeleton in OSCC. This is the first 2-hydroxyisobutylated modification proteomics performed for OSCC. Khib protein is significantly concentrated in the actin cytoskeleton regulatory pathway, indicating that this pathway may mediate the tumorigenesis or exacerbation of OSCC. SIGNIFICANCE: This is the first study that revealed the alterations of Khib protein in oral squamous cell carcinoma through LC-MS/MS-based modified proteomic. Our data showed that the protein in the actin cytoskeleton regulatory pathway was underwent significant Khib modification and abundance changes. We applied predictive function in IPA software to analyze and clarify that the aggregation of actin and the regulation of actin stability that mediated by the actin cytoskeleton regulatory pathway may be the potential mechanism of the occurrence and development of oral squamous cell carcinoma. Our research broadens the understanding of the pathogenesis of oral squamous cell carcinoma and provides new insights for future research.

RPL19 Is a Prognostic Biomarker and Promotes Tumor Progression in Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC) is one of the most common malignancies, and the therapeutic outcome remains undesirable due to its recurrence and metastasis. Gene dysregulation plays a pivotal role in the occurrence and progression of cancer, and the molecular mechanisms are largely unknown.

Methods

The differentially expressed genes of HCC screened from the GSE39791 dataset were used to conduct weighted gene co-expression network analysis. The selected hub genes were validated in The Cancer Genome Atlas (TCGA) database and 11 HCC datasets from the Gene Expression Omnibus (GEO) database. Then, a tissue microarray comprising 90 HCC specimens and 90 adjacent normal specimens was used to validate the hub genes. Moreover, the Hallmark, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were used to identify enriched pathways. Then, we conducted the immune infiltration analysis.

Results

A total of 17 co-expression modules were obtained by weighted gene co-expression network analysis. The green, blue, and purple modules were the most relevant to HCC samples. Four hub genes, RPL19, RPL35A, RPL27A, and RPS12, were identified. Interestingly, we found that all four genes were highly expressed in HCC and that their high expression was related to a poor prognosis by analyzing the TCGA and GEO databases. Furthermore, we investigated RPL19 in HCC tissue microarrays and demonstrated that RPL19 was overexpressed in tumor tissues compared with non-tumor tissues (p = 0.016). Moreover, overexpression of RPL19 predicted a poor prognosis in hepatocellular carcinoma (p < 0.0007). Then, enrichment analysis revealed that cell cycle pathways were significantly enriched, and bile acid metabolism-related pathways were significantly down-regulated when RPL19 was highly expressed. Furthermore, immune infiltration analysis showed that immune response was suppressed.

Conclusion

Our study demonstrates that RPL19 may play an important role in promoting tumor progression and is correlated with a poor prognosis in HCC. RPL19 may serve as a promising biomarker and therapeutic target for the precise diagnosis and treatment of HCC in the future.

An interactive nomogram based on clinical and molecular signatures to predict prognosis in multiple myeloma patients

Although novel drugs and treatments have been developed and improved, multiple myeloma (MM) is still recurrent and difficult to cure. In the present study, the magenta module containing 400 hub genes was determined from the training dataset of GSE24080 through weighted gene co-expression network analysis (WGCNA). Then, using the least absolute shrinkage and selection operator (Lasso) analysis, a fifteen-gene signature was firstly selected and the predictive performance for overall survival (OS) was favorable, which was identified by Receiver Operating Characteristic (ROC) curves. The risk score model was constructed based on survival-associated fifteen genes from the Lasso model, which classified MM patients into high-risk and low-risk groups. Areas under the curve (AUC) of ROC curve and log-rank test showed that the high-risk group was correlated to the dismal survival outcome of MM patients, which was also identified in testing dataset of GSE9782. The calibration plot, the AUC value of the ROC curve and Concordance-index showed that the interactive nomogram with risk score could favorably predict the probability of multi-year OS of MM patients. Therefore, it may help clinicians make a precise therapeutic decision based on the easy-to-use tool of the nomogram.

The remodelling of actin composition as a hallmark of cancer

Actin is a key structural protein that makes up the cytoskeleton of cells, and plays a role in functions such as division, migration, and vesicle trafficking. It comprises six different cell-type specific isoforms: ACTA1, ACTA2, ACTB, ACTC1, ACTG1, and ACTG2. Abnormal actin isoform expression has been reported in many cancers, which led us to hypothesize that it may serve as an early biomarker of cancer. We show an overview of the different actin isoforms and highlight mechanisms by which they may contribute to tumorigenicity. Furthermore, we suggest how the aberrant expression of actin subunits can confer cells with greater proliferation ability, increased migratory capability, and chemoresistance through incorporation into the normal cellular F-actin network and altered actin binding protein interaction. Studying this fundamental change that takes place within cancer cells can further our understanding of neoplastic transformation in multiple tissue types, which can ultimately aid in the early-detection, diagnosis and treatment of cancer.

Identification of Infertility-Associated Topologically Important Genes Using Weighted Co-expression Network Analysis

Endometriosis has been associated with a high risk of infertility. However, the underlying molecular mechanism of infertility in endometriosis remains poorly understood. In our study, we aimed to discover topologically important genes related to infertility in endometriosis, based on the structure network mining. We used microarray data from the Gene Expression Omnibus (GEO) database to construct a weighted gene co-expression network for fertile and infertile women with endometriosis and to identify gene modules highly correlated with clinical features of infertility in endometriosis. Additionally, the protein–protein interaction network analysis was used to identify the potential 20 hub messenger RNAs (mRNAs) while the network topological analysis was used to identify nine candidate long non-coding RNAs (lncRNAs). Functional annotations of clinically significant modules and lncRNAs revealed that hub genes might be involved in infertility in endometriosis by regulating G protein-coupled receptor signaling (GPCR) activity. Gene Set Enrichment Analysis showed that the phospholipase C-activating GPCR signaling pathway is correlated with infertility in patients with endometriosis. Taken together, our analysis has identified 29 hub genes which might lead to infertility in endometriosis through the regulation of the GPCR network.

Identification of potential CpG sites for oral squamous cell carcinoma diagnosis via integrated analysis of DNA methylation and gene expression

BACKGROUND

Oral squamous cell carcinoma (OSCC) accounts for more than 90% of the oral carcinomas and has a high fatality rate. This study aimed to identify potentially diagnostic biomarkers of OSCC through integrated analysis of DNA methylation and gene expression profiles.

METHODS

The DNA methylation profiles of OSCC patients from The Cancer Genome Atlas (TCGA) were analyzed to screen patients with CpG island methylator phenotype (CIMP) and investigate the relationship between CIMP and survival probability of OSCC patients. Differential methylation and expression analyses of the paired OSCC tumor and paracancerous samples from TCGA were performed. Logistic regression model was established, and the accuracy of this diagnostic model for OSCC was evaluated in validation sets from Gene Expression Omnibus (GEO).

RESULTS

OSCC patients with CIMP had lower survival probability than those without CIMP. The cg02860732 and cg04342955 were determined as candidate diagnostic methylation sites for OSCC. Logistic regression model was established based on cg02860732 and cg04342955 showed relatively high diagnostic accuracy in OSCC.

CONCLUSIONS

A diagnostic model for OSCC was identified based on the methylation sites cg02860732 and cg04342955, which might be favorable for the diagnosis of OSCC.

Identification of hub methylated-CpG sites and associated genes in oral squamous cell carcinoma

To improve personalized diagnosis and prognosis for oral squamous cell carcinoma (OSCC) by identification of hub methylated‐CpG sites and associated genes, weighted gene comethylation network analysis (WGCNA) was performed to examine and identify hub modules and CpG sites correlated with OSCC. Here, WGCNA modeling yielded blue and brown comethylation modules that were significantly associated with OSCC status. Following screening of the differentially expressed genes (DEGs) from gene expression microarrays and differentially methylated‐CpG sites (DCGs), integrated multiomics analysis of the DEGs, DCGs, and hub CpG sites from the modules was performed to investigate their correlations. Expression levels of 16 CpG sites‐associated genes were negatively correlated with methylation patterns of promoter. Moreover, Kaplan‐Meier survival analysis of the hub CpG sites and associated genes was carried out using 2 public databases, MethSurv and GEPIA. Only 5 genes, ACTA1, ACTN2, OSR1, SYNGR1, and ZNF677, had significant overall survival using GEPIA. Hypermethylated‐CpG sites ACTN2‐cg21376883 and OSR1‐cg06509239 were found to be associated with poor survival by MethSurv. Methylation status of specific site and expression levels of associated genes were determined using clinical samples by quantitative methylation‐specific PCR and real‐time PCR. Pearson's correlation analysis showed that methylation levels of cg06509239 and cg18335068 were negatively related to OSR1 and ZNF677 expression levels, respectively. Our classification schema using multiomics analysis represents a screening framework for identification of hub CpG sites and associated genes.