Integrative Multi‑omics Analysis Reveals Candidate Biomarkers for Oral Squamous Cell Carcinoma
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA775998 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR16612817_1.fastq.gz | Fastqsanger.gz | |||
| SRR16612817_2.fastq.gz | Fastqsanger.gz | |||
| SRR16612818_1.fastq.gz | Fastqsanger.gz | |||
| SRR16612818_2.fastq.gz | Fastqsanger.gz | |||
| SRR16612819_1.fastq.gz | Fastqsanger.gz |
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Project description: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.
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Project description:Gene Expression Profiling of Oral Squamous Cell Carcinoma (OSCC) was performed to delineate candidate genes clusters with potential to distinguish normal and tumor tissue from oral cavity. All tissue samples were collected after obtaining written informed consent.
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Project description:BackgroundGlioma is one of the most common malignant brain tumors and exhibits low resection rate and high recurrence risk. Although a large number of glioma studies powered by high-throughput sequencing technologies have led to massive multi-omics datasets, there lacks of comprehensive integration of glioma datasets for uncovering candidate biomarker genes.ResultsIn this study, we collected a large-scale assemble of multi-omics multi-cohort datasets from worldwide public resources, involving a total of 16,939 samples across 19 independent studies. Through comprehensive molecular profiling across different datasets, we revealed that PRKCG (Protein Kinase C Gamma), a brain-specific gene detectable in cerebrospinal fluid, is closely associated with glioma. Specifically, it presents lower expression and higher methylation in glioma samples compared with normal samples. PRKCG expression/methylation change from high to low is indicative of glioma progression from low-grade to high-grade and high RNA expression is suggestive of good survival. Importantly, PRKCG in combination with MGMT is effective to predict survival outcomes in a more precise manner.ConclusionsPRKCG bears the great potential for glioma diagnosis, prognosis and therapy, and PRKCG-like genes may represent a set of important genes associated with different molecular mechanisms in glioma tumorigenesis. Our study indicates the importance of computational integrative multi-omics data analysis and represents a data-driven scheme toward precision tumor subtyping and accurate personalized healthcare.
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Project description:Differentially methylated oral squamous cell carcinoma (OSCC) biomarkers, identified in-vitro and validated in well-characterized surgical specimens, have shown poor clinical correlation in cohorts with different risk profiles. To overcome this lack of relevance we used the HumanMethylation27 BeadChip, publicly available methylation and expression array data, and Quantitative Methylation Specific PCR to uncover differential methylation in OSCC clinical samples with heterogeneous risk profiles. A two stage-design consisting of Discovery and Prevalence screens was used to identify differential promoter methylation and deregulated pathways in patients diagnosed with OSCC and head and neck squamous cell carcinoma. Promoter methylation of KIF1A (k=0.64), HOXA9 (k=0.60), NID2 (k=0.60), and EDNRB (k=0.60) had a moderate to substantial agreement with clinical diagnosis in the Discovery screen. HOXA9 had 68% sensitivity, 100% specificity and a 0.81 AUC. NID2 had 71% sensitivity, 100% specificity and a 0.79 AUC. In the Prevalence screen HOXA9 (k=0.82) and NID2 (k=0.80) had an almost perfect agreement with histologic diagnosis. HOXA9 had 85% sensitivity, 97% specificity and a 0.95 AUC. NID2 had 87% sensitivity, 95% specificity and a 0.91 AUC. A HOXA9 and NID2 gene panel had 94% sensitivity, 97% specificity and a 0.97 AUC. In saliva from OSCC cases and controls HOXA9 had 75% sensitivity, 53% specificity and a 0.75 AUC. NID2 had 87% sensitivity, 21% specificity and a 0.73 AUC. This Phase I Biomarker Development Trial identified a panel of differentially methylated genes in normal and OSCC clinical samples from patients with heterogeneous risk profiles. This panel may be useful for early detection and cancer prevention studies. The 12 samples analyzed comprise equal numbers of three tissue-types: Primary oral squamous cell carcinoma, Oral leukoplakia and Normal oral mucosa. Gender, age and smoking-status were approximately equally represented in these goups. Normal oral mucosa served as a control.
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Project description:Microarray was used to find out the differentially expressed in tumor sites of early-stage oral squamous cell carcinoma compared with Normal parts. Furthermore, we compared cases of early-stage oral squamous cell carcinoma with lymph node metastasis with cases without lymph node metastasis. The miRNAs obtained may not only serve as predictive biomarkers for lymph node metastasis, but may also be used further to understand disease.
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Project description:Oral squamous cell carcinoma (OSCC) is a main reason of oral cancer mortality and morbidity. Cancer of oral cavity in central south Asia, ranks among third most common kinds of cancer. The discovery of candidate markers to differentiate normal from malignant cells in clinical diagnosis of OSCC would be of critical importance because this malignancy has poor prognosis. To improve the clinical outcome in OSCC patients, the present study was aimed at identifying robust candidate biomarkers for early OSCC diagnosis and to enhance understanding of the mechanisms of disease progression and pathogenesis. Of particular interest are proteins that can be found in tissue lysates of OSCC tumor vs normal adjacent mucosa samples and secreted in cell line Secretomes of HNSCC for non-invasive detection. We analysed 17 paired human malignant OSCC tissues and normal adjacent tissue in addition to secretomes of 9 HNSCC cell lines. The proteome dataset of OSCC and normal tissues consisted of 5,123 protein groups, including 299 proteins with strong differential expression (p-value <0.01, fold change barrier to ˃+2 and <-2, 205 upregulated and 94 down regulated) and 134 common proteins were also found out of total dataset of 4473 identified proteins of HNSCC cell line secretomes. Functional data analysis revealed that these differential proteins were significantly associated with multiple biological processes. Myogenesis, Fatty Acid Metabolism and KRAS Signaling DN were associated with the proteins downregulated in cancer tissues, while Protein Secretion, Unfolded Protein Response, Spliceosomal complex assembly, Protein localization to endosome and Interferon Gamma Response were enriched in the set of upregulated proteins and these regulated proteins may be classically or non-classically secreted. Furthermore, we found differential enrichment of Creb3L1, ESRRA, YY, ELF2, STAT1 and XBP transcription factors potentially regulating these major pathways.
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Project description:Thiele2013 - Oral mucosa squamous epithelial cells
The model of oral mucosa squamous epithelial cells metabolism is derived from the community-driven global reconstruction of human metabolism (version 2.02, MODEL1109130000
).
This model is described in the article:
A community-driven global reconstruction of human metabolism.
Thiele I, et al
.
Nature Biotechnology
Abstract:
Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven,
consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared
with its predecessors, the reconstruction has improved topological and functional features, including ~2x more reactions and ~1.7x more unique metabolites. Using
Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic
data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically
generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will
facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/.
This model is hosted on BioModels Database
and identified by: MODEL1310110027
.
To cite BioModels Database, please use: BioModels Database: An enhanced,
curated and annotated resource for published quantitative kinetic models
.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer
to CC0 Public Domain Dedication
for more information.
2005-01-01 | MODEL1310110027 | BioModels