Screening of potential biomarkers for chemoresistant ovarian carcinoma with miRNA expression profiling data by bioinformatics approach

Current strategies for early epithelial ovarian cancer detection using miRNA as a potential tool

Ovarian cancer is one of the most aggressive and significant malignant tumor forms in the female reproductive system. It is the leading cause of death among gynecological cancers owing to its metastasis. Since its preliminary disease symptoms are lacking, it is imperative to develop early diagnostic biomarkers to aid in treatment optimization and personalization. In this vein, microRNAs, which are short sequence non-coding molecules, displayed great potential as highly specific and sensitive biomarker. miRNAs have been extensively advocated and proven to serve an instrumental part in the clinical management of cancer, especially ovarian cancer, by promoting the cancer cell progression, invasion, delayed apoptosis, epithelial-mesenchymal transition, metastasis of cancer cells, chemosensitivity and resistance and disease therapy. Here, we cover our present comprehension of the most up-to-date microRNA-based approaches to detect ovarian cancer, as well as current diagnostic and treatment strategies, the role of microRNAs as oncogenes or tumor suppressor genes, and their significance in ovarian cancer progression, prognosis, and therapy.

NCBP2 and TFRC are novel prognostic biomarkers in oral squamous cell carcinoma

There are few prognostic biomarkers and targeted therapeutics currently in use for the clinical management of oral squamous cell carcinoma (OSCC) and patient outcomes remain poor in this disease. A majority of mutations in OSCC are loss-of-function events in tumour suppressor genes that are refractory to conventional modes of targeting. Interestingly, the chromosomal segment 3q22-3q29 is amplified in many epithelial cancers, including OSCC. We hypothesized that some of the 468 genes located on 3q22-3q29 might be drivers of oral carcinogenesis and could be exploited as potential prognostic biomarkers and therapeutic targets. Our integrative analysis of copy number variation (CNV), gene expression and clinical data from The Cancer Genome Atlas (TCGA), identified two candidate genes: NCBP2, TFRC, whose expression positively correlates with worse overall survival (OS) in HPV-negative OSCC patients. Expression of NCBP2 and TFRC is significantly higher in tumour cells compared to most normal human tissues. High NCBP2 and TFRC protein abundance is associated with worse overall, disease-specific survival, and progression-free interval in an in-house cohort of HPV-negative OSCC patients. Finally, due to a lack of evidence for the role of NCBP2 in carcinogenesis, we tested if modulating NCBP2 levels in human OSCC cell lines affected their carcinogenic behaviour. We found that NCBP2 depletion reduced OSCC cell proliferation, migration, and invasion. Differential expression analysis revealed the upregulation of several tumour-promoting genes in patients with high NCBP2 expression. We thus propose both NCBP2 and TFRC as novel prognostic and potentially therapeutic biomarkers for HPV-negative OSCC.

Constructing and validating of m7G-related genes prognostic signature for hepatocellular carcinoma and immune infiltration: potential biomarkers for predicting the overall survival

Background

To investigate the prognostic significance of N7-methylguanosine (m7G) regulators and immune infiltration in liver hepatocellular carcinoma (LIHC).

Methods

The research measured predictive m7G genes in LIHC samples from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets. Data on the stemness index based on mRNA expression (mRNAsi), gene mutations, and corresponding clinical characteristics were obtained from TCGA and ICGC. Lasso regression was used to construct the prediction model to assess the m7G prognostic signals in LIHC. Based on these genes, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to identify key biological functions and pathways. The correlation between m7G RNA methylation regulators and the prognosis and immune infiltration of LIHC was evaluated.

Results

There were 21 m7G-related differentially expressed genes (DEGs) in LIHC and healthy tissues, and LIHC patients could be divided into two categories by consensus clustering of these DEGs. A five-gene predictive approach was employed using least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Patients in the low-risk group showed a significantly higher survival rate compared with those in the high-risk group (P=0.001). Validations using the ICGC database. Also, univariate and multivariate Cox regression analyses suggested that the risk score produced by the predictive model is an independent predictor for LIHC [hazard ratio (HR): 1.848, 95% confidence interval (CI): 1.286-2.656; HR: 2.597, 95% CI: 1.358-4.965]. The ROC curves of the ICGC cohort revealed that the five-gene prediction model performed well [area under the curve (AUC) =0.642 at 1 year, AUC =0.686 at 2 years, and AUC =0.667 at 3 years]. Immuno-oncology scoring revealed that in the high-risk group, among 16 immune cells, the expressions of neutrophils and natural killer (NK) cells were low and that of regulatory T-cells (Tregs) was high.

Conclusions

LIHC occurrence and progression are linked to m7G-related genes. Corresponding prognostic models help forecast the prognosis of LIHC patients. m7G-related genes and associated immune cell infiltration in the TME may serve as potential therapeutic targets in LIHC, which requires further trials. In addition, the m7G-related gene signature offers a viable alternative to predict LIHC, and these m7G-related genes show a prospective research area for LIHC targeted treatment in the future.

The pattern of expression and prognostic value of key regulators for m7G RNA methylation in hepatocellular carcinoma

N7-methylguanosine (m7G) modification on internal RNA positions plays a vital role in several biological processes. Recent research shows m7G modification is associated with multiple cancers. However, in hepatocellular carcinoma (HCC), its implications remain to be determined. In this place, we need to interrogate the mRNA patterns for 29 key regulators of m7G RNA modification and assess their prognostic value in HCC. Initial, the details from The Cancer Genome Atlas (TCGA) database concerning transcribed gene data and clinical information of HCC patients were inspected systematically. Second, according to the mRNA profiles of 29 m7G RNA methylation regulators, two clusters (named 1 and 2, respectively) were identified by consensus clustering. Furthermore, robust risk signature for seven m7G RNA modification regulators was constructed. Last, we used the Gene Expression Omnibus (GEO) dataset to validate the prognostic associations of the seven-gene risk signature. We figured out that 24/29 key regulators of m7G RNA modification varied remarkably in their grades of expression between the HCC and the adjacent tumor control tissues. Cluster one compared with cluster two had a substandard prognosis and was also positively correlated with T classification (T), pathological stage, and vital status (fustat) significantly. Consensus clustering results suggested the expression pattern of m7G RNA modification regulators was correlated with the malignancy of HCC strongly. In addition, cluster one was extensively enriched in metabolic-related pathways. Seven optimal genes (METTL1, WDR4, NSUN2, EIF4E, EIF4E2, NCBP1, and NCBP2) were selected to establish the risk model for HCC. Indicating by further analyses and validation, the prognostic model has fine anticipating command and this probability signature might be a self supporting presage factor for HCC. Finally, a new prognostic nomogram based on age, gender, pathological stage, histological grade, and prospects were established to forecast the prognosis of HCC patients accurately. In essence, we detected association of HCC severity and expression levels of m7G RNA modification regulators, and developed a risk score model for predicting prognosis of HCC patients’ progression.

m7G Methylation-Related Genes as Biomarkers for Predicting Overall Survival Outcomes for Hepatocellular Carcinoma

Aim: The search for prognostic biomarkers and the construction of a prognostic risk model for hepatocellular carcinoma (HCC) based on N7-methyladenosine (m7G) methylation regulators. Methods: HCC transcriptomic data and clinical data were obtained from The Cancer Genome Atlas database and Shanghai Ninth People's Hospital, respectively. m7G methylation regulators were extracted, differential expression analysis was performed using the R software "limma" package, and one-way Cox regression analysis was used to screen for prognostic associations of m7G regulators. Using multi-factor Cox regression analysis, a prognostic risk model for HCC was constructed. Each patient's risk score was calculated using the model, and patients were divided into high- and low-risk groups according to the median risk score. Cox regression analysis was used to verify the validity of the model in the prognostic assessment of HCC in conjunction with clinicopathological characteristics. Results: The prognostic model was built using the seven genes, namely, CYFIP1, EIF4E2, EIF4G3, GEMIN5, NCBP2, NUDT10, and WDR4. The Kaplan-Meier survival analysis showed poorer 5-years overall survival in the high-risk group compared with the low-risk group, and the receiver-operating characteristic (ROC) curve suggested good model prediction (area under the curve AUC = 0.775, 0.820, and 0.839 at 1, 3, and 5 years). The Cox regression analysis included model risk scores and clinicopathological characteristics, and the results showed that a high-risk score was the only independent risk factor for the prognosis of patients with HCC. Conclusions: The developed bioinformatics-based prognostic risk model for HCC was found to have good predictive power.

Integrated miRNA-mRNA Analysis Reveals Potential Biomarkers of Chemoresistance in Ovarian Cancer

The current study aimed to determine potential biomarkers related to chemoresistance in ovarian cancer and the involved signaling pathways through bioinformatics analysis. This was followed by an exploration of the related indices on the occurrence and development of chemoresistance in ovarian cancer (OC). Five miRNA/mRNA expression datasets on chemoresistance OC were obtained from the Geodatabase. The significantly different expressed miRNAs (DEMs) and differently expressed genes (DEGs) between chemoresistant OC tissues and control tissues were screened using the GEO2R online tool. Afterwards, pathway analysis was utilized to analyze the DEGs and Cytoscape with STRING 11.0 was used to visualize the protein-protein interaction (PPI) network of DEGs. Afterwards, TFmiR webserver was performed to predict the TF-miRNA-mRNA network. Finally, KM-Plotter was utilized to determine the effects of hub genes and key miRNAs on survival time. A total of 24 DEMs and 548 DEGs were screened from four different datasets on chemoresistance in OC. Seven mRNA-miRNA pairs were found. Survival analysis based on the Kaplan-Meier plotter revealed that 11 biomarkers, including hsa-miR-363, hsa-miR-125b, CDKN1N, JUN, KFL4, IGFBP3, TGFBR2, CCR5, SPP1, LOX, and MMP1, which were associated with TF-miRNA-mRNA network, were closely associated with overall survival (OS) in patients with OC (P< 0.05). The integrated genomic analysis method was successful in screening novel and important genes for the occurrence and progression of chemoresistance in OC. Moreover, this method provided valuable information for investigating chemoresistance in OC and also forms the basis for further functional research.

Tumor-suppressor miRNA-27b-5p regulates the growth and metastatic behaviors of ovarian carcinoma cells by targeting CXCL1

Background

MicroRNAs (miRNAs) play crucial functions in the progression of ovarian cancer. MicroRNA-27b-5p (miR-27b-5p) has been identified as a cancer-associated miRNA. Nevertheless, the expression profile of miR-27b-5p and its functions in ovarian cancer are unexplored.

Methods

qRT-PCR and western blot analysis were used to detect the levels of miR-27b-5p and C-X-C motif chemokine ligand 1 (CXCL1). The impact of miR-27b-5p on ovarian cancer cells proliferation, migration and invasion in vitro were investigated using Cell Counting Kit-8 (CCK8), wound healing and Transwell, respectively. The expression of matrix metalloprotein-2/9 (MMP-2/9) were measured using immunofluorescence staining. Bioinformatics and luciferase reporter analysis were used to predict the target of miR-27b-5p. The growth of ovarian cancer cells in vivo was evaluated using transplanted tumor model.

Results

Here, we demonstrated that miR-27b-5p was downregulated in ovarian carcinoma cells and clinical specimens. Higher expression of miR-27b-5p was associated with an unfavorable overall survival in patients with ovarian cancer. Upregulation of miR-27b-5p decreased the viability, migration ability and invasion capacity of SKOV3 and OVCAR3 cell. MiR-27b-5p also inhibited the growth of SKOV3 cell in nude mice. Additionally, we verified that CXCL1 was a target of miR-27b-5p in ovarian carcinoma cells. Restoring the expression of CXCL1 abolished the inhibitory impacts of miR-27b-5p in ovarian cancer carcinoma cells.

Conclusion

This research revealed that miR-27b-5p restrained the progression of ovarian carcinoma possibly via targeting CXCL1.

Integrated bioinformatics analysis for the identification of potential key genes affecting the pathogenesis of clear cell renal cell carcinoma

Clear cell renal cell carcinoma (CCRCC) is a typical type of RCC with the worst prognosis among the common epithelial neoplasms of the kidney. However, its molecular pathogenesis remains unknown. Therefore, the aim of the present study was to screen for effective and potential pathogenic biomarkers of CCRCC. The gene expression profile of the GSE16441, GSE36895, GSE40435, GSE46699, GSE66270 and GSE71963 datasets were downloaded from the Gene Expression Omnibus database. First, the limma package in R language was used to identify differentially expressed genes (DEGs) in each dataset. The robust and strong DEGs were explored using the robust rank aggregation method. A total of 980 markedly robust DEGs were identified (429 upregulated and 551 downregulated). According to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, these DEGs exhibited an obvious enrichment in various cancer-related biological pathways and functions. The Search Tool for the Retrieval of Interacting Genes/Proteins database was used for the construction of a protein-protein interaction (PPI) network, the Cytoscape MCODE plug-in for module analysis and the cytoHubba plug-in to identify hub genes from the aforementioned DEGs. A total of four key modules were identified in the PPI network. A total of six hub genes, including C-X-C motif chemokine ligand 12, bradykinin receptor B2, adenylate cyclase 7, calcium sensing receptor (CASR), kininogen 1 and lysophosphatidic acid receptor 5, were identified. The DEG results of the hub genes were verified using The Cancer Genome Atlas database, and CASR was found to be significantly associated with the prognosis of patients with CCRCC. In conclusion, the present study provided new insight and potential biomarkers for the diagnosis and prognosis of CCRCC.

A meta-analysis and bioinformatics exploration of the diagnostic value and molecular mechanism of miR-193a-5p in lung cancer

Lung cancer is a leading cause of mortality worldwide and despite recent improvements in lung cancer treatments patient mortality remains high. miR-193a-5p serves a crucial role in the initiation and development of cancer; it is necessary to understand the underlying molecular mechanisms of miR-193a-5p in lung cancer, which may enable the development of improved clinical diagnoses and therapies. The present study investigated the diagnostic value of peripheral blood and tissue miR-193a-5p expression using a microarray meta-analysis. Peripheral blood miR-193a-5p was revealed to be upregulated in patients with lung cancer. The pooled area under the curve (AUC) was 0.67, with a sensitivity and specificity of 0.74 and 0.56, respectively. Conversely, the peripheral tissue miR-193a-5p expression in patients with lung cancer was significantly downregulated. The pooled AUC was 0.83, and the sensitivity and specificity were 0.65 and 0.89, respectively. Through bioinformatics analysis, three Kyoto Encyclopedia of Genes and Genomes (KEGG) terms, pathways in cancer, prostate cancer and RIG-I-like receptor signaling pathway, were identified as associated with miR-193a-5p in lung cancer. In addition, in lung cancer, six key miR-193a-5p target genes, receptor tyrosine-protein kinase erbB-2 (ERBB2), nuclear cap-binding protein subunit 2 (NCBP2), collagen α-1(I) chain (COL1A1), roprotein convertase subtilisin/kexin type 9 (PCSK9), casein kinase II subunit α (CSNK2A1) and nucleolar transcription factor 1 (UBTF), were identified, five of which were significantly upregulated (ERBB2, NCBP2, COL1A1, CSNK2A1 and UBTF). The protein expression of ERBB2, NCBP2, COL1A1, CSNK2A1 and UBTF was also upregulated. NCBP2 and CSNK2A1 were negatively correlated with miR-193a-5p. The results demonstrated that miR-193a-5p exhibited opposite expression patterns in peripheral blood and tissue. Upregulated peripheral blood miR-193a-5p and downregulated tissue miR-193a-5p may be promising diagnostic biomarkers in lung cancer. In addition, the KEGG terms pathways in cancer, prostate cancer and RIG-I-like receptor signaling pathway may suggest which pathways serve vital roles in lung cancer by regulating miR-193a-5p. In addition, six genes, ERBB2, COL1A1, PCSK9, UBTF and particularly NCBP2 and CSNK2A1, may be key target genes of miR-193a-5p in lung cancer.

FGMD: A novel approach for functional gene module detection in cancer

With the increasing availability of multi-dimensional biological datasets for the same samples (i.e., gene expression, microRNAs, copy numbers, mutations, methylations), it has now become possible to systematically understand the regulatory mechanisms operating in a cancer cell. For this task, it is important to discover a set of co-expressed genes with functions, representing a so-called functional gene module, because co-expressed genes tend to be co-regulated by the same regulators, including transcription factors, microRNAs, and copy number aberrations. Several algorithms have been used to identify such gene modules, including hierarchical clustering and non-negative matrix factorization. Although these algorithms have been applied to many microarray datasets, only a few systematic analyses of these algorithms have been performed for RNA-sequencing (RNA-Seq) data to date. Although gene expression levels determined based on microarray and RNA-Seq datasets tend to be highly correlated, the expression levels of some genes differ depending on the platforms used for analysis, which may result in the construction of different gene modules for the same samples. Here, we compare several module detection algorithms applied to both microarray and RNA-seq datasets. We further propose a new functional gene module detection algorithm (FGMD), which is based on a hierarchical clustering algorithm that was modified to reflect actual biological observations, including the fact that a single gene may be involved in multiple biological pathways. Application of existing algorithms and the new FGMD algorithm to breast cancer and ovarian cancer datasets from The Cancer Genome Atlas showed that the FGMD algorithm had the best performance for most of the functional pathway enrichment tests and in the transcription factor enrichment test. We expect that the FGMD algorithm will contribute to improving the identification of functional gene modules related to cancer.

In vivo gene expression profiling for chemosensitivity to docetaxel-cisplatin-5-FU (TPF) triplet regimen in laryngeal squamous cell carcinoma and the effect of TPF treatment on related gene expression in vitro

CONCLUSION

These results provided a battery of genes relating to TPF chemotherapeutic sensitivity and might act as molecular targets in laryngeal squamous cell carcinoma (LSCC) treatment. Moreover, these candidate biomarkers could contribute to LSCC individualized treatment.

OBJECTIVES

To screen out a set of candidate genes which could help to determine whether patients with LSCC could benefit from TPF induction chemotherapy.

METHOD

Gene-expression profiles in seven TPF-sensitive patients were compared to four resistant controls by microarray analysis. Subsequently, expression levels of potential biomarkers in chemosensitive cell line UMSCC5 after TPF treatment were observed by qRT-PCR.

RESULTS

Through microarray analysis, 1546 differently expressed genes were identified, of which 769 were up-regulated in TPF chemotherapy-responsive tissues, whereas 777 were down-regulated. Gene ontology (GO) analysis suggested these genes participating in physiological processes including cell differentiation, metabolism, signal transduction, and cellular component organization. Additionally, Kyoto Encyclopedia of Genes and Genomes (KEGG) database revealed that Wnt and p53 signaling pathways occupied important roles in TPF chemotherapeutic sensitivity. Moreover, in vitro cell culture experiments revealed the expression alternations of Mapk10, Jun, Vegfb, Pik3r5, Pld1, Tek, Itga6 exposed to TPF treatment by qRT-PCR, whilst providing an insight into the mechanism underlying TPF chemotherapeutic response in LSCC.

MicroRNA and Long Non-Coding RNA in Ovarian Carcinoma: Translational Insights and Potential Clinical Applications

Reliable biomarkers for the detection of early ovarian carcinoma are currently unavailable. MicroRNA and long non-coding RNA may be important in cancer initiation and progression by regulating gene expression through post-transcriptional mechanisms. MicroRNAs, such as miR-26a and miR-132, have been investigated as novel biomarkers for diagnosis, prognosis, monitoring of therapeutic response, and therapeutic targets in ovarian carcinomas. Some long non-coding RNAs, such as H19 and UCA1, may be involved in the pathogenesis of ovarian carcinomas. MicroRNA and long non-coding RNA have potential clinical utility in the diagnosis of ovarian cancer and predicting prognosis, metastasis, recurrence, and response to therapy.