TCGA based integrated genomic analyses of ceRNA network and novel subtypes revealing potential biomarkers for the prognosis and target therapy of tongue squamous cell carcinoma

Li, Zaiye; Jiang, Canhua; Yuan, Yongxiang

doi:10.1371/journal.pone.0216834

Cited by 24 publications

(21 citation statements)

References 58 publications

(53 reference statements)

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“…Mounting pieces of evidence has identified that aberrant genes expressions hold essential roles in tumorigenesis, also including TSCC, which are achieved through regulating its upstream miRNAs recognized as prominent modulators. [49][50][51] Emerging studies have illustrated 53 It has conclusively been shown that stronger expression of RGC-32 accentuates metastatic phenotype via mediating TGF-β-induced EMT in pancreatic cancer. 34 In the regulation of progression of lung cancer, elevation of RGC-32 enhances cell invasiveness and induces EMT via inflammation signalling.…”

Section: Discussionmentioning

confidence: 99%

Oncogenic gene RGC‐32 is a direct target of miR‐26b and facilitates tongue squamous cell carcinoma aggressiveness through EMT and PI3K/AKT signalling

Yang

Chen

et al. 2020

Cell Biochemistry & Function

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Growing data have recognized the significance of Response Gene to Complement (RGC)-32 in numerous tumour developments. Notwithstanding, the functional role and underlying mechanism of it in tongue squamous cell carcinoma (TSCC) remain enigmatic. Here, to identify the impact of RGC-32 in TSCC, its expression in multiple TSCC cells was measured and loss-of-function experiments in cell lines were performed to illuminate the function of it induced TSCC progression, via si-RNA knockdown, CCK-8, colony formation, wound-healing, transwell, flow cytometry and western blot assays. To clarify potential mechanism, expressions of hallmarks in epithelial-mesenchymal transition (EMT) process and PI3K/AKT signalling were assessed, and the upstream miR regulator of RGC-32 was predicted and verified by applying bioinformatic approaches and dual-luciferase reporter assay, respectively. Finally, the rescue experiments were applied to better elucidate the effect of miR-26b/RGC-32 axis in TSCC behaviours. As a result, RGC-32 was upregulated in TSCC cells and knocking down of it abrogated cell proliferation, trans-migration and invasion, whilst promoted apoptosis in TSCC, which was regulated through repressing EMT and inactivation of PI3K/AKT signalling. Subsequently, miR-26b was predicted and identified as an upstream regulator of RGC-32, and the pro-tumorigenic effect of RGC-32 was reversed by miR-26b overexpression. Collectively, our results demonstrated that RGC-32 facilitated TSCC progression, which was modulated by activations of PI3K/AKT pathway and EMT process, and reduction of its negative regulator of miR-26b. These findings highlight a novel role of miR-26b/RGC-32 axis in TSCC and underlying mechanism, encouraging a potent usage in TSCC treatment. Significance of the study: We first uncovered that Response Gene to Complement-32 played a significantly pro-tumorigenic role in tongue squamous cell carcinoma (TSCC), which was closely regulated by downregulation of miR-26b and activations of epithelial-mesenchymal transition process and PI3K/AKT signalling. These findings contribute to better understand the molecular mechanism in carcinogenesis of TSCC, and shed some light on promising strategy for TSCC therapeutics.

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Section: Discussionmentioning

confidence: 99%

Oncogenic gene RGC‐32 is a direct target of miR‐26b and facilitates tongue squamous cell carcinoma aggressiveness through EMT and PI3K/AKT signalling

Yang

Chen

et al. 2020

Cell Biochemistry & Function

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“…Expression spectrum. Use the Consensus Cluster Plus package [22] to perform supervised cluster analysis on the data, select samples with both methylation and expression profile data, and build a training set for building a classifier to train the model or determine model parameters; the test set is used to The performance of the final selection of the best model is tested, and the generalized ability of the trained model is tested. In short, the data set is divided into two categories to prevent overfitting.…”

Section: Discussionmentioning

confidence: 99%

DNA methylation data-based molecular subtype classification and prediction in patients with gastric cancer

et al. 2020

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Background: Genetic and epigenetic alterations have been indicated to be closely correlated with the carcinogenesis, DNA methylation is one of most frequently occurring molecular behavior that take place early during this complicated process in gastric cancer (GC). Methods: In this study, 398 samples were collected from the cancer genome atlas (TCGA) database and were analyzed, so as to mine the specific DNA methylation sites that affected the prognosis for GC patients. Moreover, the 23,588 selected CpGs that were markedly correlated with patient prognosis were used for consistent clustering of the samples into 6 subgroups, and samples in each subgroup varied in terms of M, Stage, Grade, and Age. In addition, the levels of methylation sites in each subgroup were calculated, and 347 methylation sites (corresponding to 271 genes) were screened as the intrasubgroup specific methylation sites. Meanwhile, genes in the corresponding promoter regions that the above specific methylation sites were located were performed signaling pathway enrichment analysis. Results: The specific genes were enriched to the biological pathways that were reported to be closely correlated with GC; moreover, the subsequent transcription factor enrichment analysis discovered that, these genes were mainly enriched into the cell response to transcription factor B, regulation of MAPK signaling pathways, and regulation of cell proliferation and metastasis. Eventually, the prognosis prediction model for GC patients was constructed using the Random Forest Classifier model, and the training set and test set data were carried out independent verification and test. Conclusions: Such specific classification based on specific DNA methylation sites can well reflect the heterogeneity of GC tissues, which contributes to developing the individualized treatment and accurately predicting patient prognosis.

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“…The Survival package [ 24 – 26 ] in R was used to perform survival analysis for GC patients whose subtypes were determined from the methylation profiles and the results are shown in Kaplan–Meier plots. The log-rank test was applied to compare the methylation levels in different clusters.…”

Section: Methodsmentioning

confidence: 99%

Identification of the subtypes of gastric cancer based on DNA methylation and the prediction of prognosis

Chen

et al. 2020

Clin Epigenet

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Background Gastric cancer (GC) is a digestive system cancer with a high mortality rate globally. Previous experiences and studies have provided clinicians with ample evidence to diagnose and treat patients with reasonable therapeutic options. However, there remains a need for sensitive biomarkers that can provide clues for early diagnosis and prognosis assessment. Results We found 610 independent prognosis-related 5′-cytosine-phosphate-guanine-3′ (CpG) sites (P < 0.05) among 21,121 sites in the training samples. We divided the GC samples into seven clusters based on the selected 610 sites. Cluster 6 had relatively higher methylation levels and high survival rates than the other six clusters. A prognostic risk model was constructed using the significantly altered CpG sites in cluster 6 (P < 0.05). This model could distinguish high-risk GC patients from low-risk groups efficiently with the area under the receiver operating characteristic curve of 0.92. Risk assessment showed that the high-risk patients had poorer prognosis than the low-risk patients. The methylation levels of the selected sites in the established model decreased as the risk scores increased. This model had been validated in testing group and its effectiveness was confirmed. Corresponding genes of the independent prognosis-associated CpGs were identified, they were enriched in several pathways such as pathways in cancer and gastric cancer. Among all of the genes, the transcript level of transforming growth factor β2 (TGFβ2) was changed in different tumor stages, T categories, grades, and patients’ survival states, and up-regulated in patients with GC compared with the normal. It was included in the pathways as pathways in cancer, hepatocellular carcinoma or gastric cancer. The methylation site located on the promoter of TGFβ2 was cg11976166. Conclusions This is the first study to separate GC into different molecular subtypes based on the CpG sites using a large number of samples. We constructed an effective prognosis risk model that can identify high-risk GC patients. The key CpGs sites or their corresponding genes such as TGFβ2 identified in this research can provide new clues that will enable gastroenterologists to make diagnosis or personalized prognosis assessments and better understand this disease.

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TCGA based integrated genomic analyses of ceRNA network and novel subtypes revealing potential biomarkers for the prognosis and target therapy of tongue squamous cell carcinoma

Cited by 24 publications

References 58 publications

Oncogenic gene RGC‐32 is a direct target of miR‐26b and facilitates tongue squamous cell carcinoma aggressiveness through EMT and PI3K/AKT signalling

Oncogenic gene RGC‐32 is a direct target of miR‐26b and facilitates tongue squamous cell carcinoma aggressiveness through EMT and PI3K/AKT signalling

DNA methylation data-based molecular subtype classification and prediction in patients with gastric cancer

Identification of the subtypes of gastric cancer based on DNA methylation and the prediction of prognosis

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