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

Uncovering the ceRNA Network Related to the Prognosis of Stomach Adenocarcinoma Among 898 Patient Samples

Stomach adenocarcinoma (STAD) patients are often associated with significantly high mortality rates and poor prognoses worldwide. Among STAD patients, competing endogenous RNAs (ceRNAs) play key roles in regulating one another at the post-transcriptional stage by competing for shared miRNAs. In this study, we aimed to elucidate the roles of lncRNAs in the ceRNA network of STAD, uncovering the molecular biomarkers for target therapy and prognosis. Specifically, a multitude of differentially expressed lncRNAs, miRNAs, and mRNAs (i.e., 898 samples in total) was collected and processed from TCGA. Cytoplasmic lncRNAs were kept for evaluating overall survival (OS) time and constructing the ceRNA network. Differentially expressed mRNAs in the ceRNA network were also investigated for functional and pathological insights. Interestingly, we identified one ceRNA network including 13 lncRNAs, 25 miRNAs, and 9 mRNAs. Among them, 13 RNAs were found related to the patient survival time; their individual risk score can be adopted for prognosis inference. Finally, we constructed a comprehensive ceRNA regulatory network for STAD and developed our own risk-scoring system that can predict the OS time of STAD patients by taking into account the above.

Dysregulated ceRNA network modulated by copy number variation-driven lncRNAs in breast cancer: A comprehensive analysis

Breast cancer is a malignancy harmful to physical and mental health in women, with quite high mortality. Copy number variations (CNVs) are vital factors affecting the progression of breast cancer. Detecting CNVs in breast cancer to predict the prognosis of patients has become a promising approach to accurate treatment in recent years. The differential analysis was performed on CNVs of long noncoding RNAs (lncRNAs) as well as the expression of lncRNAs, microRNAs (miRNAs) and mRNAs in normal tissue and breast tumor tissue based on The Cancer Genome Atlas (TCGA) database. The CNV-driven lncRNAs were identified by the Kruskal-Wallis test. Meanwhile, a competitive endogenous RNA (ceRNA) network regulated by CNV-driven lncRNA was constructed. As the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses revealed, the mRNAs in the dysregulated ceRNA network were mainly enriched in the biological functions and signaling pathways, including the Focal Adhesion-PI3K-Akt-mTOR-signaling pathway, the neuronal system, metapathway biotransformation Phase I and II and blood circulation, etc. The relationship between the CNVs of five lncRNAs and their gene expression in the ceRNA network was analyzed via a chi-square test, which confirmed that except for LINC00243, the expression of four lncRNAs was notably correlated with the CNVs. The survival analysis revealed that only the copy number gain of LINC00536 was evidently related to the poor prognosis of patients. The CIBERSORT algorithm showed that five lncRNAs were correlated with the abundance of immune cell infiltration and immune checkpoints. In a word, by analyzing CNV-driven lncRNAs and the ceRNA network regulated by these lncRNAs, this study explored the mechanism of breast cancer and provided novel insights into new biomarkers.

Utilization of gold nanoparticles for the detection of squamous cell carcinoma of the tongue based on laser-induced fluorescence and diffuse reflectance characteristics: an in vitro study

Squamous cell carcinoma is a very common type of oral cancer that affects the health of people with an unacceptably high mortality rate attributed to the difficulties in detecting the disease at an early stage. Therefore, effective techniques for early diagnosis and effective therapy of oral cancer are necessary. In the present study, we exploit the ability of gold nanoparticles (AuNPs) to undergo coupled surface plasmon resonance when closely spaced to improve diagnosing squamous cell carcinoma of the tongue. The prepared AuNPs are characterized by UV-VIS spectroscopy, dynamic light scattering, Fourier transform infrared spectroscopy, and transmission electron microscopy. The size of the prepared AuNPs is 12 ± 2 nm with narrow size distributions and exhibited high stability with a zeta potential of - 16.5 mV. The light fluorescence of the normal and cancer cells is recorded before and after NP addition using a spectrometer upon excitation by 405-nm laser irradiation. Furthermore, the light reflectance of the examined samples is measured at different laser wavelengths (red to NIR region). The obtained results show that the cancer cells mixed with AuNPs produce a higher fluorescence peak at 489.2 nm than the cancer cells without AuNPs. Moreover, the optical diffuse reflectance analyses reveal that the addition of AuNPs enhances cancer detection especially at the 635-nm irradiation with sensitivity (94%), specificity (87%), and overall accuracy (91%).

Long Noncoding RNA LEMD1-AS1 Increases LEMD1 Expression and Activates PI3K-AKT Pathway to Promote Metastasis in Oral Squamous Cell Carcinoma

Background. The survival rate of oral squamous cell carcinoma (OSCC) is only 50% due to a high incidence of metastasis. Long noncoding RNAs (lncRNAs) play a crucial role in OSCC genesis and progression, although their potential role in the metastasis of OSCC remains unclear. Methods. The transcriptome of 5 metastatic and 5 nonmetastatic OSCC samples were assessed by RNA sequencing. The biological functions and regulatory mechanisms of LEMD1-AS1 in OSCC were explored by in vitro and in vivo assays. Results. We identified 487 differentially expressed mRNAs (DEmRNAs) and 1507 differentially expressed lncRNAs (DElncRNAs) in OSCC with cervical lymph node (LN) metastasis relative to the nonmetastatic samples. In addition, both LEMD1-AS1 and its cognate LEMD1 were up-regulated in metastatic OSCC compared to nonmetastatic OSCC. Gain-of-function, loss-of-function, and rescue experiments indicated that LEMD1-AS1 upregulated LEMD1 to increase OSCC migration and invasion in vitro and in vivo. Mechanistically, LEMD1-AS1 stabilized LEMD1 and increased its mRNA and protein levels, and consequently activated the PI3K-AKT signaling pathway to facilitate OSCC metastasis. Conclusions. We established the lncRNA-mRNA landscape of metastatic OSCC, which indicated that LEMD1-AS1 enhanced OSCC metastasis by stabilizing its antisense transcript LEMD1. Thus, LEMD1-AS1 is a potential biomarker for predicting metastasis, as well as a therapeutic target of OSCC.

Identification of Prognosis-Related Molecular Subgroups and Construction of a Prognostic Prediction Model Using Immune-Related Genes in Pancreatic Cancer

Background

Pancreatic cancer patients with similar clinicopathological status exhibit substantially different therapeutic responses, which might be caused by the vast molecular heterogeneity of tumors. In this study, we attempted to identify specific molecular subgroups and construct a prognostic prediction model based on the expression level of immune-related genes in pancreatic cancer. The transcriptome profiling, single nucleotide variation, copy number variation, clinicopathological information, and follow-up data of pancreatic cancer patients were obtained from The Cancer Genome Atlas database. Thereafter, the immune-related genes with prognostic significance were identified for further consensus cluster analysis. The molecular characteristics and clinicopathological information were compared between the identified subgroups, and a weighted correlation network analysis was performed to identify the hub genes associated with the subgroups. Finally, the prognostic prediction model based on immune-related genes was established using the least absolute shrinkage and selection operator (LASSO) analysis.

Results

A total of 67 immune-relevant genes with prognostic significance were selected and used for the consensus cluster analysis. The total samples were divided into two groups, C1 and C2. The subgroup C1 had a significantly worse prognosis than C2, as well as lower levels of immune cell infiltration, which indicate an immunosuppressed state. The mutational rate of the cancer-related genes including KRAS, TP53, and RNF43 was higher in the C1 subgroup. The C1 subgroup was associated with more advanced tumor grade and T stage and with higher mortality. Using LASSO regression, we developed a prognostic prediction model based on the expression levels of 19 immune-related genes, which we validated in three external data sets. In addition, we identified four potential therapeutic and prognostic biomarkers (TNNT1, KCNN4, SH2D3A, and PHLDA2).

Conclusion

We identified two novel molecular subgroups of pancreatic cancer and developed a prognostic prediction model based on the expression levels of immune-related genes, which could be used in a clinical setting and could aid in unraveling the molecular processes leading to the development of pancreatic cancer.

CircSND1/miR-182-5p Axis Promotes Proliferative and Invasive Abilities of Thyroid Cancer via Binding Targeting MET

Objective. To monitor the impacts of circSND1 upon thyroid cancer (TC) tissues and cells and its mechanisms. Methods. Thiazole blue (MTT) was adopted to monitor the impacts of circSND1 upon the proliferative abilities of TPC-1 and SW1736 cells. 5-Bromodeoxyuridine (BrdU) combined with flow cytometry was adopted to monitor the impacts of circSND1 upon the DNA synthesis of TPC-1 and SW1736 cells. We adopted transwell experiment to examine the impacts of circSND1 on cell invasive abilities of TPC-1 and SW1736 cells. The mRNA quantitative levels of circSND1, miR-182-5p, and mesenchymal epidermal transformation factor (MET) in TC tissues were detected by qRT-PCR experiment. We also adopted luciferase assay to verify the targeting interaction between miR-182-5p and MET or miR-182-5p and circSND1. Results. CircSND1 mRNA and MET mRNA were upregulated in thyroid cancer tissues. MiR-182-5p quantification was attenuated in thyroid cancer tissues. Downregulation of circSND1 suppressed TC progression in vivo and in vitro. Furthermore, luciferase report assay uncovered that miR-182-5p was a direct binding target of circSND1 and MET was a direct binding target of miR-182-5p. Besides, circSND1 regulated MET expression and thyroid cancer cell function via binding miR-182-5p. Conclusion. Overexpression of circSND1 in TC tissues and cells facilitates TC tumorigenesis and metastasis via suppressing the quantitative level of miR-182-5p and inducing the upregulation of MET mRNA and protein expression, which expected to offer fresh clues for the administration of TC.

Identification of SLITRK6 as a Novel Biomarker in hepatocellular carcinoma by comprehensive bioinformatic analysis

Hepatocellular carcinoma (HCC) is the most common primary malignancy of the adult liver and morbidity are increasing in recent years, however, there is still no effective strategy to prevent and diagnose HCC. Therefore, it is urgent to research the effective biomarker to predict clinical outcomes of HCC tumorigenesis. In the current study, differentially expressed genes in HCC and normal tissues were investigated using the Gene Expression Omnibus (GEO) dataset GSE144269 and The Cancer Genome Atlas (TCGA). Gene differential expression analysis and weighted correlation network analysis (WGCNA) methods were used to identify nine and 16 key gene modules from the GEO dataset and TCGA dataset, respectively, in which the green module in the GEO dataset and magenta module in TCGA were significantly correlated with HCC occurrence. Third, the enrichment score of gene function annotation results showed that these two key modules focus on the positive regulation of inflammatory response and cell differentiation, etc. Besides, PPI network analysis, mutation analysis, and survival analysis found that SLITRK6 had high connectivity, and its mutation significantly impacted overall survival. In addition, SLITRK6 was found to be low expressed in tumor cells. To summarize, SLITRK6 mutation was found to significantly affect the occurrence and prognosis of HCC. SLITRK6 was confirmed as a new potential gene target for HCC, which may provide a new theoretical basis for personalized diagnosis and chemotherapy of HCC in the future.

Development and validation of a four-lipid metabolism gene signature for diagnosis of pancreatic cancer

Abnormal lipid metabolism is closely related to the malignant biological behavior of tumor cells. Such abnormal lipid metabolism provides energy for rapid proliferation, and certain genes related to lipid metabolism encode important components of tumor signaling pathways. In this study, we analyzed pancreatic cancer datasets from The Cancer Genome Atlas and searched for prognostic genes related to lipid metabolism in the Molecular Signature Database. A risk score model was built and verified using the GSE57495 dataset and International Cancer Genome Consortium dataset. Four molecular subtypes and 4249 differentially expressed genes (DEGs) were identified. The DEGs obtained by Weighted Gene Coexpression Network Construction analysis were intersected with 4249 DEGs to obtain a total of 1340 DEGs. The final prognosis model included CA8, CEP55, GNB3 and SGSM2, and these had a significant effect on overall survival. The area under the curve at 1, 3 and 5 years was 0.72, 0.79 and 0.87, respectively. These same results were obtained using the validation cohort. Survival analysis data showed that the model could stratify the prognosis of patients with different clinical characteristics, and the model has clinical independence. Functional analysis indicated that the model is associated with multiple cancer-related pathways. Compared with published models, our model has a higher C-index and greater risk value. In summary, this four-gene signature is an independent risk factor for pancreatic cancer survival and may be an effective prognostic indicator.

Immunological Significance of Prognostic DNA Methylation Sites in Hepatocellular Carcinoma

Background: Hepatocellular carcinoma (HCC) is a tumor with high morbidity and high mortality worldwide. DNA methylation, one of the most common epigenetic changes, might serve a vital regulatory role in cancer.

Methods: To identify categories based on DNA methylation data, consensus clustering was employed. The risk signature was yielded by systematic bioinformatics analyses based on the remarkably methylated CpG sites of cluster 1. Kaplan–Meier analysis, variable regression analysis, and ROC curve analysis were further conducted to validate the prognosis predictive ability of risk signature. Gene set enrichment analysis (GSEA) was performed for functional annotation. To uncover the context of tumor immune microenvironment (TIME) of HCC, we employed the ssGSEA algorithm and CIBERSORT method and performed TIMER database exploration and single-cell RNA sequencing analysis. Additionally, quantitative real-time polymerase chain reaction was employed to determine the LRRC41 expression and preliminarily explore the latent role of LRRC41 in prognostic prediction. Finally, mutation data were analyzed by employing the “maftools” package to delineate the tumor mutation burden (TMB).

Results: HCC samples were assigned into seven subtypes with different overall survival and methylation levels based on 5′-cytosine-phosphate-guanine-3′ (CpG) sites. The risk prognostic signature including two candidate genes (LRRC41 and KIAA1429) exhibited robust prognostic predictive accuracy, which was validated in the external testing cohort. Then, the risk score was significantly correlated with the TIME and immune checkpoint blockade (ICB)–related genes. Besides, a prognostic nomogram based on the risk score and clinical stage presented powerful prognostic ability. Additionally, LRRC41 with prognostic value was corroborated to be closely associated with TIME characterization in both expression and methylation levels. Subsequently, the correlation regulatory network uncovered the potential targets of LRRC41 and KIAA1429. Finally, the methylation level of KIAA1429 was correlated with gene mutation status.

Conclusion: In summary, this is the first to identify HCC samples into distinct clusters according to DNA methylation and yield the CpG-based prognostic signature and quantitative nomogram to precisely predict prognosis. And the pivotal player of DNA methylation of genes in the TIME and TMB status was explored, contributing to clinical decision-making and personalized prognosis monitoring of HCC.

Construction of competing endogenous RNA network and identification of novel molecular biomarkers in colon cancer: A bioinformatic analysis

Colon cancer patients suffer from high incidence and mortality rates worldwide. More novel molecular biomarkers should be used for the diagnosis and treatment of colon cancer. Long noncoding RNAs (lncRNAs) are found to be involved in colon cancer tumorigenesis and metastasis. This study aimed to identify novel lncRNAs in colon cancer.Two independent datasets (GSE70880 and GSE110715) were downloaded from the Gene Expression Omnibus database and merged with the sva package. R software was used to distinguish differentially expressed lncRNAs and mRNAs in the merged dataset. The competing endogenous RNA (ceRNA) network was constructed using differentially expressed lncRNAs and mRNAs with Cytoscape. Differentially expressed RNAs in the ceRNA network were further verified using the Cancer Genome Atlas database. Gene oncology analysis, Kyoto Encyclopedia of Genes and Genomes pathway enrichment and survival analysis were also performed to identify hub genes.A total of 99 differentially expressed lncRNAs and 95 differentially expressed mRNAs were identified in the merged database. Ten lncRNAs, 8 miRNAs, and 6 mRNAs were involved in the ceRNA network, in which LINC00114 and UCA1 were highly expressed in colon cancer. They were both associated with early tumor stages and might be used for the early diagnosis of colon cancer. High expression of LINC00114 can lead to poor overall survival of colon cancer patients. Furthermore, new pathways such as LINC00114/miR-107/PCKS5, UCA1/miR-107/PCKS5, and UCA1/miR-129-5p/SEMA6A were identified.Two novel lncRNAs (LINC00114 and UCA1) in colon cancer were identified by bioinformatics analysis. They might contribute to the occurrence and development of colon cancer. In addition, LINC00114 may be involved in the overall survival of colon cancer patients.

Melatonin sensitizes esophageal cancer cells to 5‑fluorouracil via promotion of apoptosis by regulating EZH2 expression

The present study aimed to investigate the effects of melatonin (MLT) and 5-fluorouracil (5-FU) combination on the chemotherapeutic effect of 5-FU in esophageal cancer, and determine the potential molecular mechanisms. The effects of MLT and 5-FU combination on cell proliferation, cell migration and invasion, and cell apoptosis were detected by Cell Counting Kit-8, Transwell assays and flow cytometric analysis, respectively. Quantitative PCR and western blotting were performed for mRNA and protein quantification, respectively. The present study revealed that MLT significantly inhibited cell activity in a dose-dependent manner and MLT significantly enhanced 5-FU-mediated inhibition of cell proliferation in esophageal cancer cells. Compared with the 5-FU group, the MLT and 5-FU combination group significantly inhibited the invasion and migration of EC-9706 and EC-109 cells. The present study also revealed that MLT and 5-FU synergistically promoted apoptosis via activation of the caspase-dependent apoptosis pathway. Histone-lysine N-methyltransferase EZH2 (EZH2) was highly expressed in esophageal cancer tissues and cells and its high expression promoted esophageal cancer progression. MLT and 5-FU combination inhibited cell proliferation and promoted apoptosis by regulating EZH2 expression. In conclusion, MLT enhanced 5-FU-mediated inhibition of cell proliferation via promotion of apoptosis by regulating EZH2 expression in esophageal cancer.

Eight-lncRNA signature of cervical cancer were identified by integrating DNA methylation, copy number variation and transcriptome data

BACKGROUND

Copy number variation (CNV) suggests genetic changes in malignant tumors. Abnormal expressions of long non-coding RNAs (lncRNAs) resulted from genomic and epigenetic abnormalities play a driving role in tumorigenesis of cervical cancer. However, the role of lncRNAs-related CNV in cervical cancer remained largely unclear.

METHODS

The data of messenger RNAs (mRNAs), DNA methylation, and DNA copy number were collected from 292 cervical cancer specimens. The prognosis-related subtypes of cervical cancer were determined by multi-omics integration analysis, and protein-coding genes (PCGs) and lncRNAs with subtype-specific expressions were identified. The CNV pattern of the subtype-specific lncRNAs was analyzed to identify the subtype-specific lncRNAs. A prognostic risk model based on lncRNAs was established by least absolute shrinkage and selection operator (LASSO).

RESULTS

Multi-omics integration analysis identified three molecular subtypes incorporating 617 differentially expressed lncRNAs and 1395 differentially expressed PCGs. The 617 lncRNAs were found to intersect with disease-related lncRNAs. Functional enrichment showed that 617 lncRNAs were mainly involved in tumor metabolism, immunity and other pathways, such as p53 and cAMP signaling pathways, which are closely related to the development of cervical cancer. Finally, according to CNV pattern consistent with differential expression analysis, we established a lncRNAs-based signature consisted of 8 lncRNAs, namely, RUSC1-AS1, LINC01990, LINC01411, LINC02099, H19, LINC00452, ADPGK-AS1, C1QTNF1-AS1. The interaction of the 8 lncRNAs showed a significantly poor prognosis of cervical cancer patients, which has also been verified in an independent dataset.

CONCLUSION

Our study expanded the network of CNVs and improved the understanding on the regulatory network of lncRNAs in cervical cancer, providing novel biomarkers for the prognosis management of cervical cancer patients.

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

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.

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

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.

Competing Endogenous RNAs, Non-Coding RNAs and Diseases: An Intertwined Story

MicroRNAs (miRNAs), a class of small non-coding RNA molecules, are responsible for RNA silencing and post-transcriptional regulation of gene expression. They can mediate a fine-tuned crosstalk among coding and non-coding RNA molecules sharing miRNA response elements (MREs). In a suitable environment, both coding and non-coding RNA molecules can be targeted by the same miRNAs and can indirectly regulate each other by competing for them. These RNAs, otherwise known as competing endogenous RNAs (ceRNAs), lead to an additional post-transcriptional regulatory layer, where non-coding RNAs can find new significance. The miRNA-mediated interplay among different types of RNA molecules has been observed in many different contexts. The analyses of ceRNA networks in cancer and other pathologies, as well as in other physiological conditions, provide new opportunities for interpreting omics data for the field of personalized medicine. The development of novel computational tools, providing putative predictions of ceRNA interactions, is a rapidly growing field of interest. In this review, I discuss and present the current knowledge of the ceRNA mechanism and its implications in a broad spectrum of different pathologies, such as cardiovascular or autoimmune diseases, cancers and neurodegenerative disorders.

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

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.

Targeting Oncogenic BRAF: Past, Present, and Future

Identifying recurrent somatic genetic alterations of, and dependency on, the kinase BRAF has enabled a "precision medicine" paradigm to diagnose and treat BRAF-driven tumors. Although targeted kinase inhibitors against BRAF are effective in a subset of mutant BRAF tumors, resistance to the therapy inevitably emerges. In this review, we discuss BRAF biology, both in wild-type and mutant settings. We discuss the predominant BRAF mutations and we outline therapeutic strategies to block mutant BRAF and cancer growth. We highlight common mechanistic themes that underpin different classes of resistance mechanisms against BRAF-targeted therapies and discuss tumor heterogeneity and co-occurring molecular alterations as a potential source of therapy resistance. We outline promising therapy approaches to overcome these barriers to the long-term control of BRAF-driven tumors and emphasize how an extensive understanding of these themes can offer more pre-emptive, improved therapeutic strategies.