Identification of Differentially Expressed Genes Induced by Aberrant Methylation in Oral Squamous Cell Carcinomas Using Integrated Bioinformatic Analysis

RUNX1/miR-429 feedback loop promotes growth, metastasis, and epithelial-mesenchymal transition in oral squamous cell carcinoma by targeting ITGB1

This study aimed to explore the role of miR-429 on the progression of oral squamous cell carcinoma (OSCC). OSCC cell lines were transfected with miR-429 mimic, pcDNA3.1-RUNX1, or pcDNA3.1-ITGB1, and their cell viability, apoptosis, migration, and invasion abilities were analyzed by cell counting, terminal deoxynucleotidyl transferase dUTP nick-end labeling staining, wound healing, and transwell assays, respectively. Furthermore, luciferase reporter assay, RNA pull-down, and ChIP were used to assess the regulation of miR-429, RUNX1, and ITGB1 expression in OSCC. Lastly, the biological role of the RUNX1/miR-429 feedback loop was explored in nude mice. The results revealed that miR-429 level was down-regulated, while RUNX1 and ITGB1 levels were up-regulated in OSCC tissues and that miR-429 was negatively correlated with RUNX1 and ITGB1 in OSCC tissues. Transfection of miR-429 mimic suppressed OSCC cell proliferation, migration, invasion, and epithelial-mesenchymal transition (EMT). Moreover, we found that miR-429 participated in OSCC progression by directly targeting ITGB1. Additionally, we found that RUNX1 negatively regulated miR-429 expression by binding to its promoter. Our results also revealed that the RUNX1/miR-429 feedback loop regulated ITGB1 expression and that RUNX1 overexpression rescued the inhibitory effects of miR-429 mimic on OSCC cells. In addition, miR-429 mimic significantly suppressed tumor growth, inflammatory cell infiltration, EMT, and ITGB1 expression in vivo, which were inhibited by RUNX1 overexpression. Altogether, these results indicate that the RUNX1/miR-429 feedback loop promoted growth, metastasis, and EMT in OSCC by targeting ITGB1.

Integrated bioinformatics analysis of SEMA3C in tongue squamous cell carcinoma using machine-learning strategies

Tongue squamous cell carcinoma (TSCC) is an aggressive oral cancer with a high incidence of metastasis and poor prognosis. We aim to identify and verify potential biomarkers for TSCC using bioinformatics analysis. To begin with, we examined clinical and RNA expression information of individuals with TSCC from the Gene Expression Omnibus (GEO) database. Differential expression analysis and functional analysis were conducted. Multiple machine-learning strategies were next employed to screen and determine the hub gene, and receiver operating characteristic (ROC) analysis was used to assess diagnostic value. Semaphorin3C (SEMA3C) was identified as a critical biomarker, presenting high diagnostic accuracy for TSCC. In the validation cohorts, SEMA3C exhibited high expression levels in TSCC. The high expression of SEMA3C was a poor prognostic factor in TSCC by the Kaplan-Meier curve. Based on the Gene Ontology (GO) analysis, SEMA3C was mapped in terms related to cell adhesion, positive regulation of JAK-STAT, positive regulation of stem cell maintenance, and positive regulation of NF-κB activity. Single-cell RNA sequencing (ScRNA-seq) analysis showed cells expressing SEMA3C were predominantly tumor cells. Then, we further verified that SEMA3C had high expression in TSCC clinical samples. In addition, the knockdown of SEMA3C suppressed the proliferation, migration, and invasion of TSCC cells in vitro. This study is the first to report the involvement of SEMA3C in TSCC, suggesting that upregulated SEMA3C could be a novel and critical potential biomarker for future predictive diagnostics, prevention, prognostic assessment, and personalized medical services in TSCC.

USP39 Promotes the Viability and Migration of Head and Neck Squamous Cell Carcinoma Cell by Regulating STAT1

Objective: Ubiquitin-specific peptidase 39 (USP39) plays a carcinogenic role in many cancers, but little research has been conducted examining whether it is involved in head and neck squamous cell carcinoma (HNSCC). Therefore, this study explored the functional role of USP39 in HNSCC. Method: Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to identify differentially expressed proteins (DEPs) between the HNSCC tumor and adjacent healthy tissues. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were used to assess the functional enrichment of DEPs. Immunohistochemistry was used to detect protein expression. The viability and migration of two HNSCC cell lines, namely CAL27 and SCC25, were detected using the cell counting kit-8 assay and a wound healing assay, respectively. Quantitative real-time PCR was used to detect the expression level of signal transducer and activator of transcription 1 (STAT1) mRNA. Results: LC-MS/MS results identified 590 DEPs between HNSCC and adjacent tissues collected from 4 patients. Through GO and KEGG pathway analyses, 34 different proteins were found to be enriched in the spliceosome pathway. The expression levels of USP39 and STAT1 were significantly higher in HNSCC tumor tissue than in adjacent healthy tissue as assessed by LC-MS/MS analysis, and the increased expression of USP39 and STAT1 protein was confirmed by immunohistochemistry in clinical samples collected from 7 additional patients with HNSCC. Knockdown of USP39 or STAT1 inhibited the viability and migration of CAL27 and SCC25 cells. In addition, USP39 knockdown inhibited the expression of STAT1 mRNA in these cells. Conclusion: Our findings indicated that USP39 knockdown may inhibit HNSCC viability and migration by suppressing STAT1 expression. The results of this study suggest that USP39 may be a potential new target for HNSCC clinical therapy or a new biomarker for HNSCC.

Identification and validation of DNA methylation-driven gene PCDHB4 as a novel tumor suppressor for glioblastoma diagnosis and prognosis

Aberrant DNA methylation is a critical regulator of gene expression in the development and progression of glioblastoma (GBM). However, the impact of methylation-driven gene PCDHB4 changes on GBM occurrence and progression remains unclear. Therefore, this study aimed to identify the PCDHB4 gene for early diagnosis and prognostic evaluation and clarify its functional role in GBM. Methylation-driven gene PCDHB4 was selected for GBM using the multi-omics integration method based on publicly available data sets. The diagnostic capabilities of PCDHB4 methylation and 5-hydroxymethylcytosines were validated in tissue and blood cell-free DNA (cfDNA) samples, respectively. Combined survival analysis of PCDHB4 methylation and immune infiltration cells evaluated the prognostic predictive performance of GBM patients. We identified that the PCDHB4 gene achieved high discriminative capabilities for GBM and normal tissues with an area under the curve value of 0.941. PCDHB4 hypermethylation was observed in cfDNA blood samples from GBM patients. Compared with GBM patients with PCDHB4 hypermethylation level, patients with PCDHB4 hypomethylation level had significantly poorer overall survival (p = 0.035). In addition, GBM patients with PCDHB4 hypermethylation and high infiltration of CD4+ T cell activation level had a favorable survival (p = 0.026). Moreover, we demonstrated that mRNA expression of PCDHB4 was downregulated in GBM tissues and upregulated in GBM cell lines with PCDHB4 demethylation, and PCDHB4 overexpression inhibited GBM cell proliferation and migration. In summary, we discovered a novel methylation-driven gene PCDHB4 for the diagnosis and prognosis of GBM and demonstrated that PCDHB4 is a tumor suppressor in vitro experiments.

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.

Proteome Analysis of Camellia nitidissima Chi Revealed Its Role in Colon Cancer Through the Apoptosis and Ferroptosis Pathway

Colon cancer is the third most common cancer in the world with a high mortality rate. At present, surgery combined with radiotherapy and chemotherapy is the primary treatment, but patient prognosis remains poor. Traditional Chinese medicine (TCM) has become a complementary and alternative source of anti-cancer drugs. Camellia nitidissima Chi (CNC) is a TCM used to treat a variety of cancers. However, the role of CNC in cancer remains elusive, and its effect and mechanism on colon cancer have not been reported. Here, we show that CNC exerts an excellent inhibitory effect on colon cancer proliferation and apoptosis induction in vitro and in vivo. We performed label free-based quantitative proteomic analysis to evaluate the HCT116 cells treated with CNC. Our data revealed a total of 363 differentially expressed proteins, of which 157 were up-regulated and 206 down-regulated. Gene Ontology enrichment analysis showed that these proteins were involved in tumor occurrence and development through multiple biological processes such as cell proliferation, cell apoptosis, cell cycle, and cell death. Interestingly, we also found significant changes in ferroptosis pathways. The role of essential proteins glutathione peroxidase 4 (GPX4) and heme oxygenase-1 (HMOX1) were verified. CNC decreased the expression of GPX4 and increased the expression of HMOX1 at the mRNA and protein levels in vivo and in vitro. Collectively, these findings reveal that CNC regulates colon cancer progression via the ferroptosis pathway and could be an attractive treatment for colon cancer.

Identification of Underlying Hub Genes Associated with Hypertrophic Cardiomyopathy by Integrated Bioinformatics Analysis

Background

Considered as one of the major reasons of sudden cardiac death, hypertrophic cardiomyopathy (HCM) is a common inherited cardiovascular disease. However, effective treatment for HCM is still lacking. Identification of hub gene may be a powerful tool for discovering potential therapeutic targets and candidate biomarkers.

Methods

We analysed three gene expression datasets for HCM from the Gene Expression Omnibus. Two of them were merged by “sva” package. The merged dataset was used for analysis while the other dataset was used for validation. Following this, a weighted gene coexpression network analysis (WGCNA) was performed, and the key module most related to HCM was identified. Based on the intramodular connectivity, we identified the potential hub genes. Then, a receiver operating characteristic curve analysis was performed to verify the diagnostic values of hub genes. Finally, we validated changes of hub genes, for genetic transcription and protein expression levels, in datasets of HCM patients and myocardium of transverse aortic constriction (TAC) mice.

Results

In the merged dataset, a total of 455 differentially expressed genes (DEGs) were identified from normal and hypertrophic myocardium. In WGCNA, the blue module was identified as the key module and the genes in this module showed a high positive correlation with HCM. Functional enrichment analysis of DEGs and key module revealed that the extracellular matrix, fibrosis, and neurohormone pathways played important roles in HCM. FRZB, COL14A1, CRISPLD1, LUM, and sFRP4 were identified as hub genes in the key module. These genes showed a good predictive value for HCM and were significantly up-regulated in HCM patients and TAC mice. We also found protein expression of LUM and sFRP4 increased in myocardium of TAC mice.

Conclusion

This study revealed that five hub genes are involved in the occurrence and development of HCM, and they are potentially to be used as therapeutic targets and biomarkers for HCM.

Prognostic biomarkers and therapeutic targets in oral squamous cell carcinoma: a study based on cross-database analysis

Background

Oral squamous cell carcinoma (OSCC) is a malignant cancer, the survival rate of patients is disappointing. Therefore, it is necessary to identify the driven-genes and prognostic biomarkers in OSCC.

Methods

Four Gene Expression Omnibus (GEO) datasets were integratedly analyzed using bioinformatics approaches, including identification of differentially expressed genes (DEGs), GO and KEGG analysis, construction of protein-protein interaction (PPI) network, selection of hub genes, analysis of prognostic information and genetic alterations of hub genes. ONCOMINE, The Cancer Genome Atlas (TCGA) and Human Protein Atlas databases were used to evaluate the expression and prognostic value of hub genes. Tumor immunity was assessed to investigate the functions of hub genes. Finally, Cox regression model was performed to construct a multiple-gene prognostic signature.

Results

Totally 261 genes were found to be dysregulated. 10 genes were considered to be the hub genes. The Kaplan-Meier analysis showed that upregulated SPP1, FN1, CXCL8, BIRC5, PLAUR, and AURKA were related to poor outcomes in OSCC patients. FOXM1 and TPX2 were considered as the potential immunotherapeutic targets with future clinical significance. Moreover, we constructed a nine-gene signature (TEX101, DSG2, SCG5, ADA, BOC, SCARA5, FST, SOCS1, and STC2), which can be utilized to predict prognosis of OSCC patients effectively.

Conclusion

These findings may provide new clues for exploring the molecular mechanisms and targeted therapy in OSCC. The hub genes and risk gene signature are helpful to the personalized treatment and prognostic judgement.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41065-021-00181-1.

Development and validation of 3-CpG methylation prognostic signature based on different survival indicators for colorectal cancer

Abnormal DNA methylation is considered a vital hallmark to regulate gene expression and influence the development and progression of colorectal cancer (CRC). Although CRC-related methylation prognostic models have been developed, their clinical application is limited due to the lack of external validation and extension to other survival evaluation indicators. Therefore, this study aimed to develop and validate novel methylation prognostic models correlated with different survival indicators for individualized prognosis prediction for CRC patients. The prognostic-related CpG sites of methylation-driven genes screened by the MethylMix algorithm were identified and validated in The Cancer Genome Atlas (TCGA) CRC methylation data and our methylation data. The prognostic models correlated with different survival evaluation indicators (overall survival [OS] and disease-free survival [DFS]) were developed and validated in the TCGA CRC dataset (N = 376) and our independent CRC dataset (N = 227). We utilized the combination of selected 3-CpG methylation sites in three genes (DAPP1, FAM3D, and PIGR) to construct a prognostic risk-score model. In the training dataset, Kaplan-Meier survival analysis demonstrated that high-risk patients had significantly poorer survival than low-risk patients (p_OS  = .0014; p_DFS  < .001). Then, the 3-CpG methylation signature was successfully validated as an independent predictor in the testing data set (p_OS  = .016; p_DFS  = .016). A prognostic nomogram was constructed and validated. Additionally, mediation analysis revealed the direct effect of the methylation signature on CRC prognosis (p_OS  = 9.149e-06; p_DFS  = .001). In summary, our study revealed that the 3-CpG methylation signature might be a potential prognostic indicator to facilitate individualized survival prediction for CRC patients.

Genetic Drivers of Head and Neck Squamous Cell Carcinoma: Aberrant Splicing Events, Mutational Burden, HPV Infection and Future Targets

Head and neck cancers include cancers that originate from a variety of locations. These include the mouth, nasal cavity, throat, sinuses, and salivary glands. These cancers are the sixth most diagnosed cancers worldwide. Due to the tissues they arise from, they are collectively named head and neck squamous cell carcinomas (HNSCC). The most important risk factors for head and neck cancers are infection with human papillomavirus (HPV), tobacco use and alcohol consumption. The genetic basis behind the development and progression of HNSCC includes aberrant non-coding RNA levels. However, one of the most important differences between healthy tissue and HNSCC tissue is changes in the alternative splicing of genes that play a vital role in processes that can be described as the hallmarks of cancer. These changes in the expression profile of alternately spliced mRNA give rise to various protein isoforms. These protein isoforms, alternate methylation of proteins, and changes in the transcription of non-coding RNAs (ncRNA) can be used as diagnostic or prognostic markers and as targets for the development of new therapeutic agents. This review aims to describe changes in alternative splicing and ncRNA patterns that contribute to the development and progression of HNSCC. It will also review the use of the changes in gene expression as biomarkers or as the basis for the development of new therapies.

Prognostic Significance of Cytoplasmic SPNS2 Expression in Patients with Oral Squamous Cell Carcinoma

Background and Objectives: Oral squamous cell carcinoma (OSCC) is a malignant disease with a particularly high incidence in Taiwan. Our objective in this study was to elucidate the involvement of sphingolipid transporter 2 (SPNS2) expression and SPNS2 protein expression in the clinicopathological indexes and the clinical outcomes of OSCC patients. Materials and Methods: Immunohistochemistry analysis was performed for SPNS2 protein expression in samples from 264 cases of OSCC. Correlations of SPNS2 expression with clinicopathological variables and patient survival were analyzed. Results: Our results revealed that the cytoplasmic protein expression of SPNS2 in OSCC tissue specimens was lower than in normal tissue specimens. Negative cytoplasmic protein expression of SPNS2 was significantly correlated with T status and stage. Kaplan-Meier survival curve analysis revealed that negative cytoplasmic SPNS2 expression was predictive of poorer overall survival of OSCC patients in stage III/IV. We also determined that low SPNS2 expression was an independent prognostic factor related to overall survival among OSCC patients in stage III/IV from univariate Cox proportional hazard models. Multivariate Cox proportional hazard models revealed that cytoplasmic SPNS2 expression, T status, lymph node metastasis, and histological grade were independent prognostic factors for survival. Conclusions: Overall, this study determined that SPNS2 protein may be a useful prognostic marker for OSCC patients and potential therapeutic target for OSCC treatment.

Expression and gene regulatory network of SNHG1 in hepatocellular carcinoma

Background

Small nucleolar RNA host gene 1 (SNHG1), a long noncoding RNA (lncRNA), is a transcript that negatively regulates tumour suppressor genes, such as p53. Abnormal SNHG1 expression is associated with cell proliferation and cancer. We used sequencing data downloaded from Genomic Data Commons to analyse the expression and interaction networks of SNHG1 in hepatocellular carcinoma (HCC).

Methods

Expression was examined using the limma package of R and verified by Gene Expression Profiling Interactive Analysis. We also obtained miRNA expression data from StarBase to determine the lncRNA-miRNA-mRNA–related RNA regulatory network in HCC. Kaplan–Meier (KM) analysis was performed using the survival package of R. Gene Ontology annotation of genes was carried out using Metascape.

Results

We found that SNHG1 was overexpressed and often amplified in HCC patients. In addition, SNHG1 upregulation was associated with the promotion of several primary biological functions, including cell proliferation, transcription and protein binding. Moreover, we found similar trends of small nucleolar RNA host gene 1 (SNHG1), E2F8 (E2F transcription factor 8), FANCE (FA complementation group E) and LMNB2 (encodes lamin B2) expression. In the SNHG1-associated network, high expression levels of SNHG1 (log-rank P value = 0.0643), E2F8 (log-rank P value = 0.000048), FANCE (log-rank P value = 0.00125) and LMNB2 (log-rank P value = 0.0392) were significantly associated with poor survival. Single-cell analysis showed that E2F8 may play an important role in tumorigenesis or cancer development.

Conclusions

Our results highlight the benefit of utilizing multiple datasets to understand the functional potential regulatory networks of SNHG1 and the role of SNHG1 in tumours.

Bioinformatics analysis and verification of gene targets for benign tracheal stenosis

Background

Tracheal injury could cause intratracheal scar hyperplasia which in turn causes benign tracheal stenosis (TS). With the increasing use of mechanical ventilation and ventilator, the incidence of TS is increasing. However, the molecular mechanisms of TS have not been elucidated. It is significant to further explore the molecular mechanisms of TS.

Methods

The repeatability of public data was verified. Differently expressed genes (DEGs) and most significant genes were identified between TS and normal samples. Enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were analyzed. The comparative toxicogenomics database were analyzed. TS patients were recruited and RT‐qPCR were performed to verify the most significant genes.

Results

There exist strong correlations among samples of TS and normal group. There was a total of 194 DEGs, including 61 downregulated DEGs and 133 upregulated DEGs. GO were significantly enriched in mitotic nuclear division, cell cycle, and cell division. Analysis of KEGG indicated that the top pathways were cell cycle, and p53 pathway. MKI67(OMIM:176741), CCNB1(OMIM:123836), and CCNB2(OMIM:602755) were identified as the most significant genes of TS, and validated by the clinical samples.

Conclusion

Bioinformatics methods might be useful method to explore the mechanisms of TS. In addition, MKI67, CCNB1, and CCNB2 might be the most significant genes of TS.

Identification of hub genes in hepatocellular carcinoma using integrated bioinformatic analysis

The molecular mechanisms underlying hepatocellular carcinoma (HCC) progression remain largely undefined. Here, we identified 176 commonly upregulated genes in HCC tissues based on three Gene Expression Omnibus datasets and The Cancer Genome Atlas (TCGA) cohort. We integrated survival and methylation analyses to further obtain 12 upregulated genes for validation. These genes were overexpressed in HCC tissues at the transcription and protein levels, and increased mRNA levels were related to higher tumor grades and cancer stages. The expression of all markers was negatively associated with overall and disease-free survival in HCC patients. Most of these hub genes can promote HCC proliferation and/or metastasis. These 12 hub genes were also overexpressed and had strong prognostic value in many other cancer types. Methylation and gene copy number analyses indicated that the upregulation of these hub genes was probably due to hypomethylation or increased gene copy numbers. Further, the methylation levels of three genes, KPNA2, MCM3, and LRRC1, were associated with HCC clinical features. Moreover, the levels of most hub genes were related to immune cell infiltration in HCC microenvironments. Finally, we identified three upregulated genes (KPNA2, TARBP1, and RNASEH2A) that could comprehensively and accurately provide diagnostic and prognostic value for HCC patients.

Identification of aberrantly methylated differentially expressed genes and associated pathways in endometrial cancer using integrated bioinformatic analysis

Endometrial cancer (EC) is a fatal female reproductive tumor. Bioinformatic tools are increasingly developed to screen out molecular targets related to EC. In this study, http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE17025 and http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE40032 were obtained from Gene Expression Omnibus (GEO). “limma” package and Venn diagram tool were used to identify hub genes. FunRich was used for functional analysis. Retrieval of Interacting Genes Database (STRING) was used to analyze protein‐protein interaction (PPI) complex. Cancer Genome Atlas (TCGA), GEPIA, immunohistochemistry staining, and ROC curve analysis were carried out for validation. Univariate and multivariate regression analyses were performed to predict the risk score. Compound muscle action potential (CMap) was used to find potential drugs. GSEA was also done. We retrieved seven oncogenes which were upregulated and hypomethylated and 12 tumor suppressor genes (TSGs) which were downregulated and hypermethylated. The upregulated and hypomethylated genes were strikingly enriched in term “immune response” while the downregulated and hypermethylated genes were mainly focused on term “aromatic compound catabolic process.” TCGA and GEPIA were used to screen out EDNRB, CDO1, NDN, PLCD1, ROR2, ESPL1, PRAME, and PTTG1. Among them, ESPL1 and ROR2 were identified by Cox regression analysis and were used to construct prognostic risk model. The result showed that ESPL1 was a negative independent prognostic factor. Cmap identified aminoglutethimide, luteolin, sulfadimethoxine, and maprotiline had correlation with EC. GSEA results showed that “hedgehog signaling pathway” was enriched. This research inferred potential aberrantly methylated DEGs and dysregulated pathways may participate in EC development and firstly reported eight hub genes, including EDNRB, CDO1, NDN, PLCD1, ROR2, ESPL1, PRAME, and PTTG1 that could be used to predict EC prognosis. Aminoglutethimide and luteolin may be used to fight against EC.

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.

Expression scoring of a small-nucleolar-RNA signature identified by machine learning serves as a prognostic predictor for head and neck cancer

Head and neck squamous cell carcinoma (HNSCC) is a common malignancy with high mortality and poor prognosis due to a lack of predictive markers. Increasing evidence has demonstrated small nucleolar RNAs (snoRNAs) play an important role in tumorigenesis. The aim of this study was to identify a prognostic snoRNA signature of HNSCC. Survival-related snoRNAs were screened by Cox regression analysis (univariate, least absolute shrinkage and selection operator, and multivariate). The predictive value was validated in different subgroups. The biological functions were explored by coexpression analysis and gene set enrichment analysis (GSEA). One hundred and thirteen survival-related snoRNAs were identified, and a five-snoRNA signature predicted prognosis with high sensitivity and specificity. Furthermore, the signature was applicable to patients of different sexes, ages, stages, grades, and anatomic subdivisions. Coexpression analysis and GSEA revealed the five-snoRNA are involved in regulating malignant phenotype and DNA/RNA editing. This five-snoRNA signature is not only a promising predictor of prognosis and survival but also a potential biomarker for patient stratification management.

A novel CpG-based signature for survival prediction of lung adenocarcinoma patients

Lung adenocarcinoma (LACA) is the leading cause of cancer-associated death worldwide. The present study intended to identify DNA methylation patterns that may serve as diagnostic and prognostic biomarkers for LACA. Data on DNA methylation and the survival data of the patients of LACA were obtained from The Cancer Genome Atlas. Kaplan-Meier curves and receiver operating characteristic curve analysis were utilized to build diagnostic and prognostic models. A total of 13 CpG sites were identified and validated as the optimal diagnostic and prognostic signature for overall survival. It was concluded that the CpG-based signature is a reliable predictor for the diagnosis and prognosis of patients with LACA.

Proteome and phosphoproteome reveal mechanisms of action of atorvastatin against esophageal squamous cell carcinoma

Statins comprise a class of prescription drugs used for reducing cholesterol. Evidence has also showed that statins could reduce cancer incidence. However, the anti-tumor mechanism of statins has not been fully defined. Here, we found that atorvastatin inhibited proliferation of esophageal squamous cell carcinoma (ESCC) cells. The underlying mechanisms were explored by mass spectrometry. The proteome data revealed that atorvastatin inhibited the cAMP and Rap1 signal pathways, except for Ras signal pathway. Interestingly, phosphoproteome profiles suggested that ERK^T185/Y187, CDK1^T14, and BRAC1^S1189 phosphorylation–mediated Th17 cell differentiation, Gap junction and the Platinum drug resistance pathway were down-regulated after atorvastatin treatment. The phosphorylation levels of ERK^T185/Y187, CDK1^T14 and BRAC1^S1189 were confirmed by western blotting in KYSE150 cells. More importantly, atorvastatin suppresses ESCC tumor growth in PDX models. The molecular changes in tumor tissues were confirmed by immunohistochemistry. In conclusion, deep-proteome and phosphoproteome analysis reveal a comprehensive mechanism that contributes to atorvastatin’s anti-tumor effect.

Investigation of the underlying hub genes and mechanisms of reperfusion injury in patients undergoing coronary artery bypass graft surgery by integrated bioinformatic analyses

Background

Although coronary artery bypass graft (CABG) surgery is the main method to revascularize the occluded coronary vessels in coronary artery diseases, the full benefits of the operation are mitigated by ischemia-reperfusion (IR) injury. Although many studies have been devoted to reducing IR injury in animal models, the translation of this research into the clinical field has been disappointing. Our study aimed to explore the underlying hub genes and mechanisms of IR injury.

Methods

A weighted gene co-expression network analysis (WGCNA) was executed based on the expression profiles in patients undergoing CABG surgery (GSE29396). Functional annotation and protein-protein interaction (PPI) network construction were executed within the modules of interest. Potential hub genes were predicted, combining both intramodular connectivity (IC) and degrees. Meanwhile, potential transcription factors (TFs) and microRNAs (miRNAs) were predicted by corresponding bioinformatics tools.

Results

A total of 336 differentially expressed genes (DEGs) were identified. DEGs were mainly enriched in neutrophil activity and immune response. Within the modules of interest, 5 upregulated hub genes (IL-6, CXCL8, IL-1β, MYC, PTGS-2) and 6 downregulated hub genes (C3, TIMP1, VSIG4, SERPING1, CD163, and HP) were predicted. Predicted miRNAs (hsa-miR-333-5p, hsa-miR-26b-5p, hsa-miR-124-3p, hsa-miR-16-5p, hsa-miR-98-5p, hsa-miR-17-5p, hsa-miR-93-5p) and TF (STAT1) might have regulated gene expression in the most positively related module, while hsa-miR-333-5p and HSF-1 were predicted to regulate the genes within the most negatively related module.

Conclusions

Our study illustrates an overview of gene expression changes in human atrial samples from patients undergoing CABG surgery and might help translate future research into clinical work.

A Four-Pseudogene Classifier Identified by Machine Learning Serves as a Novel Prognostic Marker for Survival of Osteosarcoma

Osteosarcoma is a common malignancy with high mortality and poor prognosis due to lack of predictive markers. Increasing evidence has demonstrated that pseudogenes, a type of non-coding gene, play an important role in tumorigenesis. The aim of this study was to identify a prognostic pseudogene signature of osteosarcoma by machine learning. A sample of 94 osteosarcoma patients’ RNA-Seq data with clinical follow-up information was involved in the study. The survival-related pseudogenes were screened and related signature model was constructed by cox-regression analysis (univariate, lasso, and multivariate). The predictive value of the signature was further validated in different subgroups. The putative biological functions were determined by co-expression analysis. In total, 125 survival-related pseudogenes were identified and a four-pseudogene (RPL11-551L14.1, HR: 0.65 (95% CI: 0.44–0.95); RPL7AP28, HR: 0.32 (95% CI: 0.14–0.76); RP4-706A16.3, HR: 1.89 (95% CI: 1.35–2.65); RP11-326A19.5, HR: 0.52(95% CI: 0.37–0.74)) signature effectively distinguished the high- and low-risk patients, and predicted prognosis with high sensitivity and specificity (AUC: 0.878). Furthermore, the signature was applicable to patients of different genders, ages, and metastatic status. Co-expression analysis revealed the four pseudogenes are involved in regulating malignant phenotype, immune, and DNA/RNA editing. This four-pseudogene signature is not only a promising predictor of prognosis and survival, but also a potential marker for monitoring therapeutic schedule. Therefore, our findings may have potential clinical significance.

Systematic Profile Analysis of Prognostic Alternative Messenger RNA Splicing Signatures and Splicing Factors in Head and Neck Squamous Cell Carcinoma

Head and neck squamous cell carcinoma (HNSC) is a common malignancy with high mortality and poor prognosis. Alternative splicing (AS) is a transcriptional regulation mechanism that generates multiple transcripts from same genes, and aberrant AS signatures of cancers can be predictive for prognosis. We identified the survival-related AS events and splicing factors (SFs) from the RNA sequencing data and the corresponding clinical information of an HNSC cohort downloaded from The Cancer Genome Atlas (TCGA) and SpliceSeq. The independent prognostic predictors were assessed by Cox proportional regression analysis, and the regulatory network of SFs and AS events was analyzed by Spearman's test and constructed. A total of 4626 survival-related AS events in 3280 genes were identified, and most were protective factors. Among the different types of splicing events, exon skip was the most frequent. The prognostic models were constructed for each type of AS, and the area under the curve of the receiver operating characteristic curve of the combined prognostic model was 0.765, indicating good predictive performance. Finally, a correlation network between SF and AS events was constructed. We identified prognostic predictors based on AS events that stratified HNSC patients into the high- and low-risk groups, and revealed splicing networks that provide insights into the underlying mechanisms.