TCGA Expedition: A Data Acquisition and Management System for TCGA Data

Comprehensive profiling of endocrine metabolism identifies a novel signature with robust predictive value in ovarian cancer

BACKGROUND

The cell endocrine pathway is a critical physiological process composed of the endoplasmic reticulum, Golgi apparatus and associated vesicles. Loss of enzymes or proteins can cause dysfunction of endoplasmic reticulum and Golgi apparatus and affect secretion pathways leading to a variety of human diseases, including cancer.

METHODS

The single-cell RNA sequencing and single nucleotide variant principal component analysis data of ovarian cancer were retrieved from The Cancer Genome Atlas and Gene Expression Omnibus (GEO) datasets. Eighty-four genes from SECRETORY_PATHWAYs were obtained from the gene set enrichment analysis (GSEA) website. Univariate cox regression analyses and ConsensusClusterPlus were used to identify prognostic genes and molecular subtypes, which were validated using the tumor immune dysfunction and exclusion (i.e. TIDE) analysis and gene mutation analysis. A prognosis model was established by randomForestSRC. Abundant infiltrated immune cells and pathway enrichment analyses were carried out, respectively, through ssGSEA, ESTIMATE, MCP-counter and GSEA. The drug sensitive analysis was performed using pRRophetic package. Immunotherapy datasets and pan-carcinoma analysis were used to examine the performance of prognostic model.

RESULTS

Eighteen prognostic genes from SECRETORY_PATHWAYs were found in both TCGA and GEO datasets. Next, two clusters (C1 and C2) were determined, for which C1 with a poor prognosis had higher immune infiltration. Tumor-related pathways, such as PATHWAYS_IN_CANCER and B_CELL_RECEPTOR_SIGNALING_PATHWAY, were enriched in C1. Moreover, C2 was suitable for immunotherapy. A four-gene (DNAJA1, NDRG3, LUZP1 and ZCCHC24) signature was developed and successfully validated. RiskScore of higher levels were significantly associated with worse prognoses. An enhanced immune infiltration, increased pathways score and inappropriate immunotherapy were observed in the high RiskScore group. The high- and low-RiskScore groups had different drug sensitivities. Immunotherapy datasets and pan-carcinoma analysis indicated that the low RiskScore group may benefit from immunotherapy.

CONCLUSIONS

Based on the perspective of the secretory signaling pathway, a robust prognostic signature with great performances was determined, which may provide clues for clinical precision treatment of ovarian cancer.

Significance of NKX2-1 as a biomarker for clinical prognosis, immune infiltration, and drug therapy in lung squamous cell carcinoma

Background

This study was performed to determine the biological processes in which NKX2-1 is involved and thus its role in the development of lung squamous cell carcinoma (LUSC) toward improving the prognosis and treatment of LUSC.

Methods

Raw RNA sequencing (RNA-seq) data of LUSC from The Cancer Genome Atlas (TCGA) were used in bioinformatics analysis to characterize NKX2-1 expression levels in tumor and normal tissues. Survival analysis of Kaplan-Meier curve, the time-dependent receiver operating characteristic (ROC) curve, and a nomogram were used to analyze the prognosis value of NKX2-1 for LUSC in terms of overall survival (OS) and progression-free survival (PFS). Then, differentially expressed genes (DEGs) were identified, and Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and Gene Set Enrichment Analysis (GSEA) were used to clarify the biological mechanisms potentially involved in the development of LUSC. Moreover, the correlation between the NKX2-1 expression level and tumor mutation burden (TMB), tumor microenvironment (TME), and immune cell infiltration revealed that NKX2-1 participates in the development of LUSC. Finally, we studied the effects of NKX2-1 on drug therapy. To validate the protein and gene expression levels of NKX2-1 in LUSC, we employed immunohistochemistry(IHC) datasets, The Gene Expression Omnibus (GEO) database, and qRT-PCR analysis.

Results

NKX2-1 expression levels were significantly lower in LUSC than in normal lung tissue. It significantly differed in gender, stage and N classification. The survival analysis revealed that high expression of NKX2-1 had shorter OS and PFS in LUSC. The multivariate Cox regression hazard model showed the NKX2-1 expression as an independent prognostic factor. Then, the nomogram predicted LUSC prognosis. There are 51 upregulated DEGs and 49 downregulated DEGs in the NKX2-1 high-level groups. GO, KEGG and GSEA analysis revealed that DEGs were enriched in cell cycle and DNA replication.The TME results show that NKX2-1 expression was positively associated with mast cells resting, neutrophils, monocytes, T cells CD4 memory resting, and M2 macrophages but negatively associated with M1 macrophages. The TMB correlated negatively with NKX2-1 expression. The pharmacotherapy had great sensitivity in the NKX2-1 low-level group, the immunotherapy is no significant difference in the NKX2-1 low-level and high-level groups. The analysis of GEO data demonstrated concurrence with TCGA results. IHC revealed NKX2-1 protein expression in tumor tissues of both LUAD and LUSC. Meanwhile qRT-PCR analysis indicated a significantly lower NKX2-1 expression level in LUSC compared to LUAD. These qRT-PCR findings were consistent with co-expression analysis of NKX2-1.

Conclusion

We conclude that NKX2-1 is a potential biomarker for prognosis and treatment LUSC. A new insights of NKX2-1 in LUSC is still needed further research.

Prognostic microRNAs as biomarkers for prostate cancer

OBJECTIVE

Prostate cancer is the second largest cancer, most commonly diagnosed in men. Several studies reveal that miRNAs (microRNAs) are involved in various stages of prostate cancer. miRNAs are a family of small non-coding RNA species that have been implicated in the post-transcriptional regulation of gene expression. The present in silico study aims at identifying miRNA biomarkers that are significantly associated with the regulation of genes involved in prostate cancer.

METHODS

Dataset of miRNA and mRNA of prostate adenocarcinoma patients and controls was downloaded from The Cancer Genome Atlas (TCGA), and differential gene expression analysis was carried out. ROC and Kaplan-Meier survival analyses were performed on differentially expressed miRNAs. Pathway analysis was carried out for significant miRNAs, and protein-protein interaction of involved genes and miRNAs was examined.

RESULTS

A total of 185 miRNAs were differentially expressed between the patients and the control. ROC and Kaplan-Meier survival analysis showed that the two miRNAs hsa-mir-133b and hsa-mir-17-5p were found to be significantly associated with prostate cancer prognosis. HAS2 and EPHA10 gene targets of identified miRNA were also differentially expressed. A protein-protein interaction (PPI) network was constructed, and the HAS2 gene was found to be interacting with the epidermal growth factor receptor (EGFR).

CONCLUSION

This study highlights the potential of hsa-mir-133b and hsa-mir-17-5p miRNAs as biomarkers for the prognosis of prostate cancer. However, further experimental studies are required to validate this finding.

MicroRNA‑606 inhibits the growth and metastasis of triple‑negative breast cancer by targeting Stanniocalcin 1

Triple‑negative breast cancer (TNBC) is associated with a poor prognosis; however, treatments for TNBC are limited, with poor outcomes. MicroRNAs (miRNAs/miRs) are small non‑coding RNA molecules that are able to regulate gene expression. The present study aimed to identify differentially expressed miRNAs in patients with breast cancer, and to investigate the functional role of the identified miRNA targets and their effects in vitro and in vivo. Transfection with miR‑606 suppressed TNBC cell proliferation, migration, invasion and tumor sphere‑forming ability, as determined using trypan blue, Transwell and sphere formation assays. Moreover, miR‑606 induced the apoptosis of TNBC cells, as determined by flow cytometric analysis. Furthermore, intratumoral injections of miR‑606 mimics suppressed tumor growth in MDA‑MB‑231 xenografts. In addition, MDA‑MB‑231 cells transfected with miR‑606 mimics exhibited decreased lung metastatic nodules in a mouse tail vein injection model. Notably, miR‑606 and STC1 expression had opposing effects on the overall survival of patients with TNBC. The results of the present study suggested a novel tumor suppressor function for miR‑606 in TNBC, thus indicating its potential application in the development of anticancer miRNA therapeutics.

CCDC103 as a Prognostic Biomarker Correlated with Tumor Progression and Immune Infiltration in Glioma

Background

The Coiled-coil domain-containing proteins (CCDCs) are expressed in many cancers, but the role of Coiled-coil domain-containing protein 103 (CCDC103) in cancers remains unclear. Further investigations are necessary to ascertain its diagnostic significance and understand its biological function in cancers. This study aims to elucidate the biological functionalities of CCDC103 in glioma and evaluate the correlation between CCDC103 expression with glioma progression.

Methods

Clinical data on glioma patients were acquired from The Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA), and the Gene Expression Omnibus (GEO). The evaluation encompassed the examination of correlations between CCDC103 expression, pathological characteristics, and clinical outcomes. Furthermore, the analysis included the assessment of the correlations between CCDC103 expression and immune cell infiltration as well as glioma progression.

Results

Gliomas have higher levels of CCDC103 expression than the para-carcinoma tissues. Poorer prognosis, unfavorable histological characteristics, the absence of IDH gene mutations, and the absence of chromosome 1p and 19q deletions were all associated with higher expression of CCDC103 in gliomas. In addition to patient age, tumor grade, the absence of IDH mutations, and the absence of chromosome 1p and 19q deletions, univariate and multivariate Cox analyses showed that CCDC103 expression was independently prognostic of overall survival, disease-free survival, and progression-free survival in patients with glioma. Furthermore, tumor infiltration of B cells, neutrophils, macrophages, and dendritic cells were all linked with elevated expression of CCDC103. High CCDC103 expression was linked to immune response-related signaling pathways and cell proliferation, according to gene set enrichment analysis (GSEA). Notably, the knockdown of CCDC103 in glioma cell lines resulted in a significant reduction in cell proliferation and migration.

Conclusion

The correlation between CCDC103 expression and both glioma progression and immune cell infiltration implies that CCDC103 expression holds promise as a valuable prognostic biomarker for glioma.

CESCProg: a compact prognostic model and nomogram for cervical cancer based on miRNA biomarkers

Cervical squamous cell carcinoma, more commonly cervical cancer, is the fourth common cancer among women worldwide with substantial burden of disease, and less-invasive, reliable and effective methods for its prognosis are necessary today. Micro-RNAs are increasingly recognized as viable alternative biomarkers for direct diagnosis and prognosis of disease conditions, including various cancers. In this work, we addressed the problem of systematically developing an miRNA-based nomogram for the reliable prognosis of cervical cancer. Towards this, we preprocessed public-domain miRNA -omics data from cervical cancer patients, and applied a cascade of filters in the following sequence: (i) differential expression criteria with respect to controls; (ii) significance with univariate survival analysis; (iii) passage through dimensionality reduction algorithms; and (iv) stepwise backward selection with multivariate Cox modeling. This workflow yielded a compact prognostic DEmiR signature of three miRNAs, namely hsa-miR-625-5p, hs-miR-95-3p, and hsa-miR-330-3p, which were used to construct a risk-score model for the classification of cervical cancer patients into high-risk and low-risk groups. The risk-score model was subjected to evaluation on an unseen test dataset, yielding a one-year AUROC of 0.84 and five-year AUROC of 0.71. The model was validated on an out-of-domain, external dataset yielding significantly worse prognosis for high-risk patients. The risk-score was combined with significant features of the clinical profile to establish a predictive prognostic nomogram. Both the miRNA-based risk score model and the integrated nomogram are freely available for academic and not-for-profit use at CESCProg, a web-app (https://apalania.shinyapps.io/cescprog).

Integrative analysis of mitochondrial metabolic reprogramming in early-stage colon and liver cancer

Gastrointestinal malignancies, including colon adenocarcinoma (COAD) and liver hepatocellular carcinoma (LIHC), remain leading causes of cancer-related deaths worldwide. To better understand the underlying mechanisms of these cancers and identify potential therapeutic targets, we analyzed publicly accessible Cancer Genome Atlas datasets of COAD and LIHC. Our analysis revealed that differentially expressed genes (DEGs) during early tumorigenesis were associated with cell cycle regulation. Additionally, genes related to lipid metabolism were significantly enriched in both COAD and LIHC, suggesting a crucial role for dysregulated lipid metabolism in their development and progression. We also identified a subset of DEGs associated with mitochondrial function and structure, including upregulated genes involved in mitochondrial protein import and respiratory complex assembly. Further, we identified mitochondrial 3-hydroxy-3-methylglutaryl-CoA synthase (HMGCS2) as a crucial regulator of cancer cell metabolism. Using a genome-scale metabolic model, we demonstrated that HMGCS2 suppression increased glycolysis, lipid biosynthesis, and elongation while decreasing fatty acid oxidation in colon cancer cells. Our study highlights the potential contribution of dysregulated lipid metabolism, including ketogenesis, to COAD and LIHC development and progression and identifies potential therapeutic targets for these malignancies.

Analysis and validation of the potential of the MYO1E gene in pancreatic adenocarcinoma based on a bioinformatics approach

Pancreatic adenocarcinoma (PAAD) is a common digestive cancer, and its prognosis is poor. Myosin 1E (MYO1E) is a class I myosin family member whose expression and function have not been reported in PAAD. In the present study, bioinformatics analysis was used to explore the expression levels of MYO1E in PAAD and its prognostic value, and the immunological role of MYO1E in PAAD was analyzed. The study revealed that a variety of malignancies have substantially increased MYO1E expression. Further investigation demonstrated that PAAD tissues exhibited greater levels of MYO1E mRNA and protein expression than normal tissues. High MYO1E expression is associated with poor prognosis in patients with PAAD. MYO1E expression was also associated with pathological stage in patients with PAAD. Functional enrichment analysis demonstrated that MYO1E was linked to multiple tumor-related mechanisms in PAAD. The pancreatic adenocarcinoma tumor microenvironment (TME) was analyzed and it was revealed that MYO1E expression was positively associated with tumor immune cell infiltration. In addition, MYO1E was closely associated with some tumor chemokines/receptors and immune checkpoints. In vitro experiments revealed that the suppression of MYO1E expression could inhibit pancreatic adenocarcinoma cell proliferation, invasion and migration. Through preliminary analysis, the present study evaluated the potential function of MYO1E in PAAD and its function in TME, and MYO1E may become a potential biomarker for PAAD.

PLA2R1 Inhibits Differentiated Thyroid Cancer Proliferation and Migration via the FN1-Mediated ITGB1/FAK Axis

PLA2R1 is a novel gene that is aberrantly expressed in a variety of malignancies. However, the role and mechanism of PLA2R1 in thyroid cancer has not been elucidated. We aimed to uncover the underlying mechanism of PLA2R1 in thyroid cancer. We collected 115 clinical specimens, including 54 tumor tissues and 61 para-cancerous tissues, who underwent surgical treatment at Shanghai Tenth Hospital. Immunohistochemical staining was used to evaluate PLA2R1 expression in differentiated thyroid cancer (DTC) tissues. The thyroid cancer cell lines 8505c and FTC133 transfected with PLA2R1 overexpression or knockdown plasmids were used for CCK8 assays and a wound healing assay. Next, we conducted coimmunoprecipitation (Co-IP) experiments and western blotting to explore the underlying mechanism of PLA2R1 in regulating the growth of thyroid cancer. We discovered that the expression of PLA2R1 was lower in the tumor tissues than in para-cancerous tissues (χ² = 37.0, p < 0.01). The overexpression of PLA2R1 significantly suppressed thyroid cancer cell proliferation and migration, and both of these effects were partially attenuated by the knockdown of PLA2R1. Furthermore, the in vivo growth of DTC could be alleviated by the knockdown of PLA2R1. The mechanistic study revealed that PLA2R1 competed with FN1 for binding to ITGB1, inhibiting the FAK axis and epithelial-mesenchymal transition (EMT). We speculate that PLA2R1 might be a promising marker and a novel therapeutic target for thyroid cancer.

Data Provenance in Biomedical Research: Scoping Review

BACKGROUND

Data provenance refers to the origin, processing, and movement of data. Reliable and precise knowledge about data provenance has great potential to improve reproducibility as well as quality in biomedical research and, therefore, to foster good scientific practice. However, despite the increasing interest on data provenance technologies in the literature and their implementation in other disciplines, these technologies have not yet been widely adopted in biomedical research.

OBJECTIVE

The aim of this scoping review was to provide a structured overview of the body of knowledge on provenance methods in biomedical research by systematizing articles covering data provenance technologies developed for or used in this application area; describing and comparing the functionalities as well as the design of the provenance technologies used; and identifying gaps in the literature, which could provide opportunities for future research on technologies that could receive more widespread adoption.

METHODS

Following a methodological framework for scoping studies and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, articles were identified by searching the PubMed, IEEE Xplore, and Web of Science databases and subsequently screened for eligibility. We included original articles covering software-based provenance management for scientific research published between 2010 and 2021. A set of data items was defined along the following five axes: publication metadata, application scope, provenance aspects covered, data representation, and functionalities. The data items were extracted from the articles, stored in a charting spreadsheet, and summarized in tables and figures.

RESULTS

We identified 44 original articles published between 2010 and 2021. We found that the solutions described were heterogeneous along all axes. We also identified relationships among motivations for the use of provenance information, feature sets (capture, storage, retrieval, visualization, and analysis), and implementation details such as the data models and technologies used. The important gap that we identified is that only a few publications address the analysis of provenance data or use established provenance standards, such as PROV.

CONCLUSIONS

The heterogeneity of provenance methods, models, and implementations found in the literature points to the lack of a unified understanding of provenance concepts for biomedical data. Providing a common framework, a biomedical reference, and benchmarking data sets could foster the development of more comprehensive provenance solutions.

Identification of PTK2 as an adverse prognostic biomarker in breast cancer by integrated bioinformatics and experimental analyses

PTK2 is highly expressed in many cancers and is involved in cell growth, survival, migration, and invasion. However, the prognostic value of PTK2 and its potential function remain unclear in breast cancer. Therefore, we performed a comprehensive analysis of multiple public databases to explore the roles of PTK2. By integrating multiple datasets, we found that PTK2 mRNA expression in breast cancer tissue was higher than that in normal breast tissue or adjacent tissue. High PTK2 expression was associated with lymph node metastasis stage, tumor stage, breast cancer type, age, TP53 mutation, and gender and significantly predicted a poor survival outcome in breast cancer patients. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) results suggested that PTK2 and co-expressed genes participated in the cell cycle. Immune infiltration analysis clarified that high PTK2 expression was positively correlated with infiltrating levels of CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells. The DNA methylation of PTK2 in breast cancer tissues was higher than that in normal tissues, and high PTK2 methylation was correlated with poor prognosis in breast cancer patients. Furthermore, 16 possible ceRNA networks related to PTK2 were constructed for breast cancer. Additionally, PTK2 knockdown could suppress the proliferation and migration ability of MCF-7 cells. These results suggest that PTK2 can be used as a prognostic biomarker for breast cancer.

Artificial intelligence unifies knowledge and actions in drug repositioning

Drug repositioning aims to reuse existing drugs, shelved drugs, or drug candidates that failed clinical trials for other medical indications. Its attraction is sprung from the reduction in risk associated with safety testing of new medications and the time to get a known drug into the clinics. Artificial Intelligence (AI) has been recently pursued to speed up drug repositioning and discovery. The essence of AI in drug repositioning is to unify the knowledge and actions, i.e. incorporating real-world and experimental data to map out the best way forward to identify effective therapeutics against a disease. In this review, we share positive expectations for the evolution of AI and drug repositioning and summarize the role of AI in several methods of drug repositioning.

Bioinformatics analysis identifies DYNC1I1 as prognosis marker in male patients with liver hepatocellular carcinoma

Background

Liver hepatocellular carcinoma (LIHC) is one of the most common malignant tumors. However, the etiology and exact molecular mechanism of LIHC are still not fully understood, which makes it urgent for us to further study the molecular events behind.

Methods

In this study, differences in mRNA expression between LIHC samples and normal adjacent samples were found through analyzing the TCGA database, and key targets were sought. We analyzed 371 LIHC samples and 50 normal adjacent samples according to P <0.01 and logFC>2.5, a total of 1092 genes were identified differentially expressed, including 995 up-regulated genes and 97 down-regulated genes. We predicted the interactions of these differentially expressed mRNAs, and used Cyto-Hubba to locate the hub gene-dynein cytoplasmic 1 intermediate chain 1 (DYNC1I1).

Results

Survival analysis showed that DYNC1I1 was a prognostic factor for LIHC male patients. Functional enrichment indicated that DYNC1I1 and differentially expressed interacting proteins were involved in the cell cycle.

Conclusion

In conclusion, this study discovers that DYNC1I1 can be used as a prognostic marker for LIHC male patients.

Identifying Cancer Drivers Using DRIVE: A Feature-Based Machine Learning Model for a Pan-Cancer Assessment of Somatic Missense Mutations

Sporadic cancer develops from the accrual of somatic mutations. Out of all small-scale somatic aberrations in coding regions, 95% are base substitutions, with 90% being missense mutations. While multiple studies focused on the importance of this mutation type, a machine learning method based on the number of protein-protein interactions (PPIs) has not been fully explored. This study aims to develop an improved computational method for driver identification, validation and evaluation (DRIVE), which is compared to other methods for assessing its performance. DRIVE aims at distinguishing between driver and passenger mutations using a feature-based learning approach comprising two levels of biological classification for a pan-cancer assessment of somatic mutations. Gene-level features include the maximum number of protein-protein interactions, the biological process and the type of post-translational modifications (PTMs) while mutation-level features are based on pathogenicity scores. Multiple supervised classification algorithms were trained on Genomics Evidence Neoplasia Information Exchange (GENIE) project data and then tested on an independent dataset from The Cancer Genome Atlas (TCGA) study. Finally, the most powerful classifier using DRIVE was evaluated on a benchmark dataset, which showed a better overall performance compared to other state-of-the-art methodologies, however, considerable care must be taken due to the reduced size of the dataset. DRIVE outlines the outstanding potential that multiple levels of a feature-based learning model will play in the future of oncology-based precision medicine.

PD-1 Coexpression Gene Analysis and the Regulatory Network in Endometrial Cancer Based on Bioinformatics Analysis

Gynecological malignancies are tumors of the female reproductive system, mainly cervical cancer, endometrial cancer, and ovarian cancer. Endometrial cancer (EC) is the most common gynecological malignant tumor in developed countries. The aim of this study was to construct a network of programmed cell death protein 1 (PD-1) coexpressed genes through bioinformatics analysis and screen the potential biomarkers of PD-1 in endometrial cancer. In addition, genes and pathways involved in PD-1 and modulating tumor immune status were identified. We select the EC transcriptomic dataset in TCGA to retrieve gene sets on the cBioPortal platform, and the PD-1 coexpressed genes were obtained on the platform. GO and KEGG enrichment analysis of coexpressed genes was performed using the DAVID database. The target protein-protein interaction (PPI) network was constructed using Cytoscape 3.7.1 software, and the hub genes were then screened. A total of 976 coexpression genes were obtained. The enrichment analysis showed that PD-1 coexpressed genes were significantly enriched in overall components of the cell structure, the interaction of cytokines with cytokine receptors, chemokine signaling pathways, and cell adhesion molecules (CAMs). Ten hub genes were obtained by node degree analysis. CD3E gene is involved in the prognosis and immune process of EC, and the expression level is related to PD-1 (Pearson correlation coefficient is 0.82, P < 0.01). Patients with low CD3E gene expression in EC have a poor prognosis. The coexpression hub genes of PD-1 are related to immunity, in which CD3E is a prognostic marker that is involved in the PD-1/PD-L1-induced tumor immune escape. This study provides a new area to study the mechanism of PD-1/PD-L1 in EC and the precise treatment with targeted drugs.

Prediction of prognosis of patients with lung cancer in combination with the immune score

PURPOSE

The host's immune response to malignant tumor is fundamental to tumorigenesis and tumor development. The immune score is currently used to assess prognosis and to guide immunotherapy; however, its association with lung cancer prognosis is not clear.

METHODS

Clinical features and immune score data of lung cancer patients from The Cancer Genome Atlas were obtained to build a clinical prognosis nomogram. The model's accuracy was verified by calibration curves.

RESULTS

In total, 1005 patients with lung cancer were included. Patients were divided into three groups according to low, medium, and high immune scores. Compared with patients in the low immune score group, the disease-free survival (DFS) of patients in medium and high immune score groups was significantly longer; the hazard ratio (HR) and 95% confidence interval (95% CI) were 0.77 [0.60-0.99] and 0.74 [0.60-0.91], respectively. The overall survival (OS) of patients in the medium and high immune score groups was significantly longer than in the low immune score group; the HR and 95% CI were 0.74 [0.57-0.96] and 0.69 [0.55-0.88], respectively. A clinical prediction model was established to predict the survival prognosis. As verified by calibration curves, the model showed good predictive ability, especially for predicting 3-/5-year DFS and OS.

CONCLUSION

Patients with lung cancer with medium and high immune scores had longer DFS and OS than those in low immune score group. Patient prognosis can be effectively predicted by the clinical prediction model combining clinical features and immune score and was consistent with actual clinical outcomes.

Artificial intelligence in oncology: From bench to clinic

In the past few years, Artificial Intelligence (AI) techniques have been applied to almost every facet of oncology, from basic research to drug development and clinical care. In the clinical arena where AI has perhaps received the most attention, AI is showing promise in enhancing and automating image-based diagnostic approaches in fields such as radiology and pathology. Robust AI applications, which retain high performance and reproducibility over multiple datasets, extend from predicting indications for drug development to improving clinical decision support using electronic health record data. In this article, we review some of these advances. We also introduce common concepts and fundamentals of AI and its various uses, along with its caveats, to provide an overview of the opportunities and challenges in the field of oncology. Leveraging AI techniques productively to provide better care throughout a patient's medical journey can fuel the predictive promise of precision medicine.

To Determine Pivotal Genes Driven by Methylated DNA in Obstructive Sleep Apnea Hypopnea Syndrome

Obstructive sleep apnea syndrome (OSAHS) is a widespread respiratory dysfunction that has attracted more and more attention in recent years. Recently, a large number of studies have shown that abnormal DNA methylation epigenetically silences genes necessary for the pathogenesis of human diseases. However, the exact mechanism of abnormal DNA methylation in OSAHS is still elusive. In this study, we downloaded the OSAHS data from the GEO database. Our data for the first time revealed 520 hypermethylated genes and 889 hypomethylated genes in OSAHS. Bioinformatics analysis revealed that these abnormal methylated genes exhibited an association with the regulation of angiogenesis, apoptosis, Wnt, and ERBB2 signaling pathways. PPI network analysis displayed the interactions among these genes and validated several hub genes, such as GPSM2, CCR8, TAS2R20, TAS2R4, and TAS2R5, which were related to regulating liganded Gi-activating GPCR and the transition of mitotic metaphase/anaphase. In conclusion, our study offers a new hint of understanding the molecular mechanisms in OSAHS progression and will provide OSAHS with newly generated innovative biomarkers.

Bioinformatics analysis of differentially expressed miRNAs in non-small cell lung cancer

OBJECTIVE

Non-small cell lung cancer (NSCLC) contains 85% of lung cancer. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) are the largest NSCLC subgroups. The aim of the study was to investigate the underlying mechanism in developing more effective subtype-specific molecular therapeutic procedures.

METHODS

A total of 876 specimens were used in this study: 494 LUAD tissues (ie, 449 LUAD tissues and 45 matched normal tissues) and 382 LUSC tissues (ie, 337 LUSC tissues and 45 matched normal tissues). The miRNA sequencing data were processed using R. The differential expressed miRNAs between lung cancer and normal tissues were analyzed using the limma package in R. Gene expression, Western blotting, hematoxylin and eosin staining, and luciferase assay were used to test LUAD and LUSC.

RESULTS

LUAD and LUSC appear sharply distinct at molecular and pathological level. Let-7a-5p, miR-338, miR-375, miR-217, miR-627, miR-140, miR-147b, miR-138-2, miR-584, and miR-197 are top 10 relevant miRNAs and CLDN3, DSG3, KRT17, TMEM125, KRT5, NKX2-1, KRT7, ABCC5, KRAS, and PLCG2 are top 10 relevant genes in NSCLC. At the same time, the miRNAs expression levels were also quite different between the two groups. Among the differential expressed miRNAs, let-7a-5p was significantly down-regulated in LUAD while miR-338 was markedly down-regulated in LUSC. Bioinformatics analyses appeared that let-7a-5p directly targets high-molecular weight keratin 5 (KRT5) which were shown to be a strong risk factor for LUAD. And NK2 homeobox 1(NKX2-1) which was associated with tumor progression in LUSC was identified as a target gene of miR-338.

CONCLUSIONS

Distinct profile of miRNAs can take a part in the development of LUAD and LUSC and thus could serve as a subtype-specific molecular therapeutic target to protect against LUAD and LUSC.

Five gene signatures were identified in the prediction of overall survival in resectable pancreatic cancer

Background

Although genes have been previously detected in pancreatic cancer (PC), aberrant genes that play roles in resectable pancreatic cancer should be further assessed.

Methods

Messenger RNA samples and clinicopathological data corrected with PC were downloaded from The Cancer Genome Atlas (TCGA). Resectable PC patients were randomly divided into a primary set and a validation set. Univariable Cox regression analysis, lasso-penalized Cox regression analysis, and multivariable Cox analysis were implemented to distinguish survival-related genes (SRGs). A risk score based on the SRGs was calculated by univariable Cox regression analysis. A genomic-clinical nomogram was established by integrating the risk score and clinicopathological data to predict overall survival (OS) in resectable PC.

Results

Five survival-related genes (AADAC, DEF8, HIST1H1C, MET, and CHFR) were significantly correlated with OS in resectable PC. The resectable PC patients, based on risk score, were sorted into a high-risk group that showed considerably unfavorable OS (p < 0.001) than the low-risk group, in both the primary set and the validation set. The concordance index (C-index) was calculated to evaluate the predictive performance of the nomogram were respectively in the primary set [0.696 (0.608–0.784)] and the validation set [0.682 (0.606–0.758)]. Additionally, gene set enrichment Analysis discovered several meaningful enriched pathways.

Conclusion

Our study identified five prognostic gene biomarkers for OS prediction and which facilitate postoperative molecular target therapy for the resectable PC, especially the nomic-clinical nomogram which may be used as an effective model for the postoperative OS evaluation and also an optimal therapeutic tool for the resectable PC.

Identification of a nine-gene prognostic signature for gastric carcinoma using integrated bioinformatics analyses

BACKGROUND

Gastric carcinoma (GC) is one of the most aggressive primary digestive cancers. It has unsatisfactory therapeutic outcomes and is difficult to diagnose early.

AIM

To identify prognostic biomarkers for GC patients using comprehensive bioinformatics analyses.

METHODS

Differentially expressed genes (DEGs) were screened using gene expression data from The Cancer Genome Atlas and Gene Expression Omnibus databases for GC. Overlapping DEGs were analyzed using univariate and multivariate Cox regression analyses. A risk score model was then constructed and its prognostic value was validated utilizing an independent Gene Expression Omnibus dataset (GSE15459). Multiple databases were used to analyze each gene in the risk score model. High-risk score-associated pathways and therapeutic small molecule drugs were analyzed and predicted, respectively.

RESULTS

A total of 95 overlapping DEGs were found and a nine-gene signature (COL8A1, CTHRC1, COL5A2, AADAC, MAMDC2, SERPINE1, MAOA, COL1A2, and FNDC1) was constructed for the GC prognosis prediction. Receiver operating characteristic curve performance in the training dataset (The Cancer Genome Atlas-stomach adenocarcinoma) and validation dataset (GSE15459) demonstrated a robust prognostic value of the risk score model. Multiple database analyses for each gene provided evidence to further understand the nine-gene signature. Gene set enrichment analysis showed that the high-risk group was enriched in multiple cancer-related pathways. Moreover, several new small molecule drugs for potential treatment of GC were identified.

CONCLUSION

The nine-gene signature-derived risk score allows to predict GC prognosis and might prove useful for guiding therapeutic strategies for GC patients.

Downregulation of miR‑193a‑3p via targeting cyclin D1 in thyroid cancer

Thyroid cancer (TC) is a frequently occurring malignant tumor with a rising steadily incidence. microRNA (miRNA/miR)‑193a‑3p is an miRNA that is associated with tumors, playing a crucial role in the genesis and progression of various cancers. However, the expression levels of miR‑193a‑3p and its molecular mechanisms in TC remain to be elucidated. The present study aimed to probe the expression of miR‑193a‑3p and its clinical significance in TC, including its underlying molecular mechanisms. Microarray and RNA sequencing data gathered from three major databases, specifically Gene Expression Omnibus (GEO), ArrayExpress and The Cancer Genome Atlas (TCGA) databases, and the relevant data from the literature were used to examine miR‑193a‑3p expression. Meta‑analysis was also conducted to evaluate the association between clinicopathological parameters and miR‑193a‑3p in 510 TC and 59 normal samples from the TCGA database. miRWalk 3.0, and the TCGA and GEO databases were used to predict the candidate target genes of miR‑193a‑3p. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes and protein‑protein interaction network enrichment analyses were conducted by using the predicted candidate target genes to investigate the underlying carcinogenic mechanisms. A dual luciferase assay was performed to validate the targeting regulatory association between the most important hub gene cyclin D1 (CCND1) and miR‑193a‑3p. miR‑193a‑3p expression was considerably downregulated in TC compared with in the non‑cancer controls (P<0.001). The area under the curve of the summary receiver operating characteristic was 0.80. Downregulation of miR‑193a‑3p was also significantly associated with age, sex and metastasis (P=0.020, 0.044 and 0.048, respectively). Bioinformatics analysis indicated that a low miR‑193a‑3p expression may augment CCND1 expression to affect the biological processes of TC. In addition, CCND1, as a straightforward target, was validated through a dual luciferase assay. miR‑193a‑3p and CCND1 may serve as prognostic biomarkers of TC. Finally, miR‑193a‑3p may possess a crucial role in the genesis and progression of TC by altering the CCND1 expression.

Identification of Key Differentially Expressed Transcription Factors in Glioblastoma

Glioblastoma (GBM) is the most frequent malignant brain tumor in adults. Our study focused on uncovering differentially expressed genes (DEGs) and their methylation in order to identify novel diagnostic biomarkers and potential treatment targets. Using GBM RNA-sequencing data from The Cancer Genome Atlas (TCGA) database, DEGs between GBM samples and paracancer tissue samples were analyzed. Enrichment analysis for DEGs and transcription factors (TFs) was performed. A total of 1029 upregulated genes and 1542 downregulated genes were identified, which were associated mainly with multiple tumor-related and immune-related pathways such as cell cycle, mitogen-activated protein kinase signaling pathway, leukocyte transendothelial migration, and autoimmune thyroid disease. These DEGs were enriched for 174 TFs, and six TFs were differentially expressed and identified as key TFs in GBM: HOXA3, EN1, ZIC1, and FOXD3 were upregulated, while HLF and EGR3 were downregulated. A total of 1978 DEGs were involved in the regulatory networks of the six key differentially expressed TFs. High expression of EN1 was associated with shorter overall survival, while high expression of EGR3 was associated with shorter recurrence-free survival. The six TFs were differentially methylated in GBM samples compared with paracancer tissues. Our study identifies numerous DEGs and their associated pathways as potential contributors to GBM, particularly the TFs EN1, EGR3, HOXA3, ZIC1, FOXD3, and HLF. The differential expression of these TFs may be unlikely driven by aberrant methylation. These TFs may be useful as diagnostic markers and treatment targets in GBM, and EN1 and EGR3 may have predictive prognostic value.

The Application of Deep Learning in Cancer Prognosis Prediction

Deep learning has been applied to many areas in health care, including imaging diagnosis, digital pathology, prediction of hospital admission, drug design, classification of cancer and stromal cells, doctor assistance, etc. Cancer prognosis is to estimate the fate of cancer, probabilities of cancer recurrence and progression, and to provide survival estimation to the patients. The accuracy of cancer prognosis prediction will greatly benefit clinical management of cancer patients. The improvement of biomedical translational research and the application of advanced statistical analysis and machine learning methods are the driving forces to improve cancer prognosis prediction. Recent years, there is a significant increase of computational power and rapid advancement in the technology of artificial intelligence, particularly in deep learning. In addition, the cost reduction in large scale next-generation sequencing, and the availability of such data through open source databases (e.g., TCGA and GEO databases) offer us opportunities to possibly build more powerful and accurate models to predict cancer prognosis more accurately. In this review, we reviewed the most recent published works that used deep learning to build models for cancer prognosis prediction. Deep learning has been suggested to be a more generic model, requires less data engineering, and achieves more accurate prediction when working with large amounts of data. The application of deep learning in cancer prognosis has been shown to be equivalent or better than current approaches, such as Cox-PH. With the burst of multi-omics data, including genomics data, transcriptomics data and clinical information in cancer studies, we believe that deep learning would potentially improve cancer prognosis.

Candidate lncRNA-microRNA-mRNA networks in predicting non-small cell lung cancer and related prognosis analysis

PURPOSE

The role of non-coding RNA, once thought to be dark matter, is increasingly prominent in cancer. Our article explores the effect of non-coding RNA in lung adenocarcinoma and lung squamous cell carcinoma by mining TCGA public database.

METHODS

Download the data by applying the official TCGA software. The data were analyzed by R data analysis packages, 'edgeR', 'gplots' and 'survival'. We better illustrate the potential networks of lung cancer genes by constructing ceRNAs, using Cytoscape software.

RESULTS

We obtained genes which were differentially expressed in lung adenocarcinoma and lung squamous cell carcinoma analysis. Within these differentially expressed genes, we also conducted a survival analysis to find differentially expressed genes associated with prognosis in both lung adenocarcinoma and lung squamous cell carcinoma. Based on genes differentially expressed of both lung adenocarcinoma and lung squamous cell carcinoma, we constructed a ceRNA network to illustrate the mechanism of lung adenocarcinoma and lung squamous cell carcinoma. Our study analyzed genes which were differentially expressed in lung adenocarcinoma and lung squamous cell carcinoma using the TCGA database.

CONCLUSION

Based on this, the prognosis in both lung squamous cell carcinoma and lung adenocarcinoma was analyzed. We have also constructed a ceRNA network to provide a basis for the study of ceRNA in lung adenocarcinoma and lung squamous cell carcinoma.

A novel microRNA signature for pathological grading in lung adenocarcinoma based on TCGA and GEO data

Lung adenocarcinoma (LUAD) is one of the most common types of lung cancer and its poor prognosis largely depends on the tumor pathological stage. Critical roles of microRNAs (miRNAs) have been reported in the tumorigenesis and progression of lung cancer. However, whether the differential expression pattern of miRNAs could be used to distinguish early-stage (stage I) from mid-late-stage (stages II–IV) LUAD tumors is still unclear. In this study, clinical information and miRNA expression profiles of patients with LUAD were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases. TCGA-LUAD (n=470) dataset was used for model training and validation, and the GSE62182 (n=94) and GSE83527 (n=36) datasets were used as external independent test datasets. The diagnostic model was created through miRNA feature selection followed by SVM classifier and was confirmed by 5-fold cross-validation. A receiver operating characteristic curve was calculated to evaluate the accuracy and robustness of the model. Using the DX score and LIBSVM tool, a 16-miRNA signature that could distinguish LUAD pathological stages was identified. The area under the curve rates were 0.62 [95% confidence interval (CI): 0.56–0.67], 0.66 (95% CI: 0.54–0.76) and 0.63 (95% CI: 0.43–0.82) in TCGA-LUAD internal validation dataset, the GSE62182 external validation dataset, and the GSE83527 external validation dataset, respectively. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology enrichment analyses suggested that the target genes of the 16-miRNA signature were mainly involved in metabolic pathways. The present findings demonstrate that a 16-miRNA signature could serve as a promising diagnostic biomarker for pathological staging in LUAD.

Two miRNA prognostic signatures of head and neck squamous cell carcinoma: A bioinformatic analysis based on the TCGA dataset

MicroRNAs(miRNAs) are maladjusted in multifarious malignant tumor and can be considered as both carcinogens and tumor‐inhibiting factor. In the present study, we analyzed the miRNAs expression profiles and clinical information of 481 patients with head and neck squamous cell carcinoma (HNSCC) through the TCGA dataset to identify the prognostic miRNAs signature. A total of 114 significantly differentially expressed miRNAs (SDEMs) were identified, consisting of 60 up‐adjusted and 54 down‐adjusted miRNAs. The Kaplan‐Meier survival method identified the prognostic function of 2 miRNAs (miR‐4652‐5p and miR‐99a‐3P). Univariate and multivariate Cox regression analyses indicated that the 2 miRNAs were significant prognostic elements of HNSCC. Furthermore, bioinformatic analysis was conducted by means of 4 online gene predicted toolkits to recognize the target genes, and enrichment analysis was performed on the target genes by DAVID. The outcomes depicted that target genes were correlated with calcium, as well as cell proliferation, circadian entrainment, EGFR, PI3K‐Akt‐mTOR, and P53 signaling pathways. Finally, the PPI network was conducted in view of STRING database and Cytoscape. Eight hub genes were identified by CytoHubba and MCODE app, respectively, CBL, SKP1, H2AFX, HGF, POLR2F, UBE2I, VAMP2, and GNAI2 genes. As a result, we identified 2 miRNAs signatures, 8 hub genes, and significant signaling pathways for estimating the prognosis of HNSCC. In order to further explore the molecular mechanism of HNSCC occurrence and development, more comprehensive basic and clinical studies are needed.

A Novel Three-miRNA Signature Identified Using Bioinformatics Predicts Survival in Esophageal Carcinoma

Objective

We identified differentially expressed microRNAs (DEMs) between esophageal carcinoma (ESCA) tissues and normal esophageal tissues. We then constructed a novel three-miRNA signature to predict the prognosis of ESCA patients using bioinformatics analysis. Materials and Methods. We combined two microarray profiling datasets from the Gene Expression Omnibus (GEO) database and RNA-seq datasets from the Cancer Genome Atlas (TCGA) database to analyze DEMs in ESCA. The clinical data from 168 ESCA patients were selected from the TCGA database to assess the prognostic role of the DEMs. The TargetScan, miRDB, miRWalk, and DIANA websites were used to predict the miRNA target genes. Functional enrichment analysis was conducted using the Database for Annotation, Visualization, and Integrated Discovery (David), and protein-protein interaction (PPI) networks were obtained using the Search Tool for the Retrieval of Interacting Genes database (STRING).

Results

With cut-off criteria of P < 0.05 and |log2FC| > 1.0, 33 overlapping DEMs, including 27 upregulated and 6 downregulated miRNAs, were identified from GEO microarray datasets and TCGA RNA-seq count datasets. The Kaplan–Meier survival analysis indicated that a three-miRNA signature (miR-1301-3p, miR-431-5p, and miR-769-5p) was significantly associated with the overall survival of ESCA patients. The results of univariate and multivariate Cox regression analysis showed that the three-miRNA signature was a potential prognostic factor in ESCA. Furthermore, the gene functional enrichment analysis revealed that the target genes of the three miRNAs participate in various cancer-related pathways, including viral carcinogenesis, forkhead box O (FoxO), vascular endothelial growth factor (VEGF), human epidermal growth factor receptor 2 (ErbB2), and mammalian target of rapamycin (mTOR) signaling pathways. In the PPI network, three target genes (MAPK1, RB1, and CLTC) with a high degree of connectivity were selected as hub genes.

Conclusions

Our results revealed that a three-miRNA signature (miR-1301-3p, miR-431-5p, and miR-769-5p) is a potential novel prognostic biomarker for ESCA.

Use of Natural Language Processing to Extract Clinical Cancer Phenotypes from Electronic Medical Records

Current models for correlating electronic medical records with -omics data largely ignore clinical text, which is an important source of phenotype information for patients with cancer. This data convergence has the potential to reveal new insights about cancer initiation, progression, metastasis, and response to treatment. Insights from this real-world data will catalyze clinical care, research, and regulatory activities. Natural language processing (NLP) methods are needed to extract these rich cancer phenotypes from clinical text. Here, we review the advances of NLP and information extraction methods relevant to oncology based on publications from PubMed as well as NLP and machine learning conference proceedings in the last 3 years. Given the interdisciplinary nature of the fields of oncology and information extraction, this analysis serves as a critical trail marker on the path to higher fidelity oncology phenotypes from real-world data.

Identification of a five-miRNA signature predicting survival in cutaneous melanoma cancer patients

Background

Cutaneous melanoma (CM) is the deadliest form of skin cancer. Numerous studies have revealed that microRNAs (miRNAs) are expressed abnormally in melanoma tissues. Our work aimed to assess multiple miRNAs using bioinformatic analysis in order to predict the prognoses of cutaneous melanoma patients.

Methods

The microarray dataset GSE35579 was downloaded from the Gene Expression Omnibus (GEO) database to detect the differential expression of miRNAs (DEMs), including 41 melanoma (primary and metastatic) tissues and 11 benign nevi. Clinical information and miRNA sequencing data of cutaneous melanoma tissues were downloaded from the Cancer Genome Atlas database (TCGA) to assess the prognostic values of DEMs. Additionally, the target genes of DEMs were anticipated using miRanda, miRmap, TargetScan, and PicTar. Finally, functional analysis was performed using selected target genes on the Annotation, Visualization and Integrated Discovery (DAVID) website.

Results

After performing bioinformatic analysis, a total of 185 DEMs were identified: 80 upregulated miRNAs and 105 downregulated miRNAs. A five-miRNA (miR-25, miR-204, miR-211, miR-510, miR-513c) signature was discovered to be a potential significant prognostic biomarker of cutaneous melanoma when using the Kaplan–Meier survival method (P = 0.001). Univariate and multivariate Cox regression analyses showed that the five-miRNA signature could be an independent prognostic marker (HR = 0.605, P = 0.006) in cutaneous melanoma patients. Biological pathway analysis indicated that the target genes may be involved in PI3K-Akt pathways, ubiquitin-mediated proteolysis, and focal adhesion.

Conclusion

The identified five-miRNA signature may serve as a prognostic biomarker, or as a potential therapeutic target, in cutaneous melanoma patients.

Long noncoding RNA ZFAS1 promotes progression of papillary thyroid carcinoma by sponging miR-590-3p and upregulating HMGA2 expression

Background

Thyroid cancer is the most common endocrine malignancy, papillary thyroid carcinoma (PTC) is the main form of thyroid cancer. The long non-coding RNA (lncRNA) zinc finger antisense 1 (ZFAS1) is highly expressed in various cancer tissues and it has been shown to function as a tumor promoter in various cellular processes. However, the role of ZFAS1 in PTC is not well understood currently. Thus, this study aimed to explore the potential roles of ZFAS1 in the development and progression of PTC.

Material and methods

PTC tissues (n=80) and noncancerous tissues were collected. Gain- and loss-of-function assays were performed to determine the effect of ZFAS1 on proliferation in K-1 and TPC-1 cells. The ZFAS1/mir-590-3P/HMGA2 aixs were analysed in PTC cell lines.

Results

We found that the expression of ZFAS1 was increased in PTC tissues and four PTC cell lines (B-CPAP, IHH-4, TPC-1, and K-1). The gain- and loss-of-function assays showed that overexpressing ZFAS1 promoted cell proliferation and inhibited cell apoptosis in PTC cells in vitro. We demonstrated that knockdown of ZFAS1 inhibits tumor growth and upregulation of ZFAS1 promotes tumor growth in vivo. Bioinformatics analysis revealed that miR-590-3p targeted the 3ʹ-UTR of ZFAS1. The double luciferase reporter and RNA-binding protein immunoprecipitation assay demonstrated that miR-590-3p is a target of ZFAS1. Rescue experiments confirmed that miR-590-3p could reverse the effect of ZFAS1 on PTC cells. Moreover, we identified high mobility group AT-hook 2 (HMGA2) to be a downstream target of miR-590-3p and ZFAS1 which activates HMGA2 expression by sponging to miR-590-3p.

Conclusion

High ZFAS1 expression level was associated with the progression of PTC, and ZFAS1 contributed to PTC progression via miR-590-3p/HMGA2 regulatory aixs. Therefore, ZFAS1 might be a potential therapeutic target for PTC intervention.

A three-gene novel predictor for improving the prognosis of cervical cancer

Cervical cancer is the most common gynecological malignancy, the third most common malignant tumor in women worldwide, and the most common malignant tumor among Chinese women. However, despite continuous improvement in medical treatment, the number of cervical cancer cases in China is on the increase annually, consistent with the general trend in global cervical cancer incidence. Therefore, it is particularly important to study the pathogenesis of cervical cancer at the genetic level in China. The aim of the present study was to use the TCGA database to identify potential genetic signatures that could predict the prognosis of patients with cervical cancer and provide evidence supporting clinical genetic intervention in cervical cancer. Primarily, an effective three-gene signature was found that predicts prognosis in patients with cervical cancer. This model can provide prima facie evidence for future assessment of patient risk and prognosis, but further testing is required to improve its accuracy. Our results also suggested that centromere protein M, methionine sulfoxide reductase B3 and Zic family member 2 could be promising biomarkers for the prognosis of cervical cancer.

Identification of a Combined RNA Prognostic Signature in Adenocarcinoma of the Lung

Background

Adenocarcinoma of the lung is a type of non-small cell lung cancer (NSCLC). Clinical outcome is associated with tumor grade, stage, and subtype. This study aimed to identify RNA expression profiles, including long noncoding RNA (lncRNA), microRNA (miRNA), and mRNA, associated with clinical outcome in adenocarcinoma of the lung using bioinformatics data.

Material/Methods

The miRNA and mRNA expression profiles were downloaded from The Cancer Genome Atlas (TCGA) database, and lncRNA expression profiles were downloaded from The Atlas of Noncoding RNAs in Cancer (TANRIC) database. The independent dataset, the Gene Expression Omnibus (GEO) accession dataset, GSE81089, was used. RNA expression profiles were used to identify comprehensive prognostic RNA signatures based on patient survival time.

Results

From 7,704 lncRNAs, 787 miRNAs, and 28,937 mRNAs of 449 patients, four joint RNA molecular signatures were identified, including RP11-909N17.2, RP11-14N7.2 (lncRNAs), MIR139 (miRNA), KLHDC8B (mRNA). The random forest (RF) classifier was used to test the prediction ability of patient survival risk and showed a good predictive accuracy of 71% and also showed a significant difference in overall survival (log-rank P=0.0002; HR, 3.54; 95% CI, 1.74–7.19). The combined RNA signature also showed good performance in the identification of patient survival in the validation and independent datasets.

Conclusions

This study identified four RNA sequences as a prognostic molecular signature in adenocarcinoma of the lung, which may also provide an increased understanding of the molecular mechanisms underlying the pathogenesis of this malignancy.

MicroRNA signature predicts survival in papillary thyroid carcinoma

Papillary thyroid cancer (PTC) accounts for the majority of malignant thyroid tumors. Recently, several microRNA (miRNA) expression profiling studies have used bioinformatics to suggest miRNA signatures as potential prognostic biomarkers in various malignancies. However, a prognostic miRNA biomarker has not yet been established for PTC. The aim of the present study was to identify miRNAs with prognostic value for the overall survival (OS) of patients with PTC by analyzing high-throughput miRNA data and their associated clinical characteristics downloaded from The Cancer Genome Atlas database. From our dataset, 150 differentially expressed miRNAs were identified between tumor and nontumor samples; of these miRNAs, 118 were upregulated and 32 were downregulated. Among the 150 differentially expressed miRNAs, a four miRNA signature was identified that reliably predicts OS in patients with PTC. This miRNA signature was able to classify patients into a high-risk group and a low-risk group with a significant difference in OS (P < .01). The prognostic value of the signature was validated in a testing set ( P < .01). The four miRNA signature was an independent prognostic predictor according to the multivariate analysis and demonstrated good performance in predicting 5-year disease survival with an area under the receiver operating characteristic curve area under the curve (AUC) score of 0.886. Thus, this signature may serve as a novel biomarker for predicting the survival of patients with PTC.

Systems Biology Approaches and Precision Oral Health: A Circadian Clock Perspective

A vast majority of the pathophysiological and metabolic processes in humans are temporally controlled by a master circadian clock located centrally in the hypothalamic suprachiasmatic nucleus of the brain, as well as by specialized peripheral oscillators located in other body tissues. This circadian clock system generates a rhythmical diurnal transcriptional-translational cycle in clock genes and protein expression and activities regulating numerous downstream target genes. Clock genes as key regulators of physiological function and dysfunction of the circadian clock have been linked to various diseases and multiple morbidities. Emerging omics technologies permits largescale multi-dimensional investigations of the molecular landscape of a given disease and the comprehensive characterization of its underlying cellular components (e.g., proteins, genes, lipids, metabolites), their mechanism of actions, functional networks and regulatory systems. Ultimately, they can be used to better understand disease and interpatient heterogeneity, individual profile, identify personalized targetable key molecules and pathways, discover novel biomarkers and genetic alterations, which collectively can allow for a better patient stratification into clinically relevant subgroups to improve disease prediction and prevention, early diagnostic, clinical outcomes, therapeutic benefits, patient's quality of life and survival. The use of “omics” technologies has allowed for recent breakthroughs in several scientific domains, including in the field of circadian clock biology. Although studies have explored the role of clock genes using circadiOmics (which integrates circadian omics, such as genomics, transcriptomics, proteomics and metabolomics) in human disease, no such studies have investigated the implications of circadian disruption in oral, head and neck pathologies using multi-omics approaches and linking the omics data to patient-specific circadian profiles. There is a burgeoning body of evidence that circadian clock controls the development and homeostasis of oral and maxillofacial structures, such as salivary glands, teeth and oral epithelium. Hence, in the current era of precision medicine and dentistry and patient-centered health care, it is becoming evident that a multi-omics approach is needed to improve our understanding of the role of circadian clock-controlled key players in the regulation of head and neck pathologies. This review discusses current knowledge on the role of the circadian clock and the contribution of omics-based approaches toward a novel precision health era for diagnosing and treating head and neck pathologies, with an emphasis on oral, head and neck cancer and Sjögren's syndrome.

Cancer Genetic Network Inference Using Gaussian Graphical Models

The Cancer Genome Atlas (TCGA) provides a rich resource that can be used to understand how genes interact in cancer cells and has collected RNA-Seq gene expression data for many types of human cancer. However, mining the data to uncover the hidden gene-interaction patterns remains a challenge. Gaussian graphical model (GGM) is often used to learn genetic networks because it defines an undirected graphical structure, revealing the conditional dependences of genes. In this study, we focus on inferring gene interactions in 15 specific types of human cancer using RNA-Seq expression data and GGM with graphical lasso. We take advantage of the corresponding Kyoto Encyclopedia of Genes and Genomes pathway maps to define the subsets of related genes. RNA-Seq expression levels of the subsets of genes in solid cancerous tumor and normal tissues were extracted from TCGA. The gene expression data sets were cleaned and formatted, and the genetic network corresponding to each cancer type was then inferred using GGM with graphical lasso. The inferred networks reveal stable conditional dependences among the genes at the expression level and confirm the essential roles played by the genes that encode proteins involved in the two key signaling pathway phosphoinositide 3-kinase (PI3K)/AKT/mTOR and Ras/Raf/MEK/ERK in human carcinogenesis. These stable dependences elucidate the expression level interactions among the genes that are implicated in many different human cancers. The inferred genetic networks were examined to further identify and characterize a collection of gene interactions that are unique to cancer. The cross-cancer genetic interactions revealed from our study provide another set of knowledge for cancer biologists to propose strong hypotheses, so further biological investigations can be conducted effectively.

New functionalities in the TCGAbiolinks package for the study and integration of cancer data from GDC and GTEx

The advent of Next-Generation Sequencing (NGS) technologies has opened new perspectives in deciphering the genetic mechanisms underlying complex diseases. Nowadays, the amount of genomic data is massive and substantial efforts and new tools are required to unveil the information hidden in the data. The Genomic Data Commons (GDC) Data Portal is a platform that contains different genomic studies including the ones from The Cancer Genome Atlas (TCGA) and the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) initiatives, accounting for more than 40 tumor types originating from nearly 30000 patients. Such platforms, although very attractive, must make sure the stored data are easily accessible and adequately harmonized. Moreover, they have the primary focus on the data storage in a unique place, and they do not provide a comprehensive toolkit for analyses and interpretation of the data. To fulfill this urgent need, comprehensive but easily accessible computational methods for integrative analyses of genomic data that do not renounce a robust statistical and theoretical framework are required. In this context, the R/Bioconductor package TCGAbiolinks was developed, offering a variety of bioinformatics functionalities. Here we introduce new features and enhancements of TCGAbiolinks in terms of i) more accurate and flexible pipelines for differential expression analyses, ii) different methods for tumor purity estimation and filtering, iii) integration of normal samples from other platforms iv) support for other genomics datasets, exemplified here by the TARGET data. Evidence has shown that accounting for tumor purity is essential in the study of tumorigenesis, as these factors promote confounding behavior regarding differential expression analysis. With this in mind, we implemented these filtering procedures in TCGAbiolinks. Moreover, a limitation of some of the TCGA datasets is the unavailability or paucity of corresponding normal samples. We thus integrated into TCGAbiolinks the possibility to use normal samples from the Genotype-Tissue Expression (GTEx) project, which is another large-scale repository cataloging gene expression from healthy individuals. The new functionalities are available in the TCGAbiolinks version 2.8 and higher released in Bioconductor version 3.7.

The advent of Next-Generation Sequencing (NGS) technologies has been generating a massive amount of data which require continuous efforts in developing and maintain computational tool for data analyses. The Genomic Data Commons (GDC) Data Portal is a platform that contains different cancer genomic studies. Such platforms have often the primary focus on the data storage and they do not provide a comprehensive toolkit for analyses. To fulfil this urgent need, comprehensive but accessible computational protocols that do not renounce a robust statistical framework are thus required. In this context, we here present the new functions of the R/Bioconductor package TCGAbiolinks to improve the discovery of differentially expressed genes in cancer and tumor (sub)types, include the estimate of tumor purity and tumor infiltrations, use normal samples from other platforms and support more broadly other genomics datasets.

Identification of miR-375 as a potential prognostic biomarker for esophageal squamous cell cancer: A bioinformatics analysis based on TCGA and meta-analysis

Accumulating evidence has demonstrated that aberrantly expressed miRNAs in cancer tissues regulated various cellular processes related to carcinogenesis. The present study aimed to identify the differentially expressed miRNAs between esophageal squamous cell cancer (ESCC) and adjacent normal esophageal tissue (ANET). In our present study, we identified 129 differentially expressed miRNAs between ESCC and ANET by analyzing high-throughput miRNA data downloaded from TCGA database. After investigating the prognostic value of the 129 differential expressed miRNAs, eight miRNAs were found to be associated with prognosis of patients with ESCC. The clinical significance and bio-function of miR-375 was further examined. We performed Gene Set Enrichment Analysis (GSEA) to identify the top three gene sets that significantly altered between the patients with miR-375 low expression and high expression. In order to explore the mechanism of the development and progression of ESCC, the role of miR-375 in ESCC and its four candidate target genes was examined. At last, we performed a meta-analysis to verify the prognostic value of miR-375 in ESCC. In conclusion, our findings suggest that miR-375 serves as a promising independent prognostic factor for ESCC and function as a tumor suppressor.

The effect of centromere protein U silencing by lentiviral mediated RNA interference on the proliferation and apoptosis of breast cancer

Centromere protein U (CENPU) is a novel transcriptional repressor that is associated with different types of cancer. However, its function in breast cancer is poorly understood. In the present study, it was identified that CENPU was highly expressed in breast cancer tissues compared with expression in normal breast tissues (P=0.001). Furthermore, the CENPU mRNA level in tumors was often elevated, compared with the matched adjacent normal breast cancer tissue specimens in the dataset from The Cancer Genome Atlas database (n=106; P<0.001). To understand the function of CENPU in human breast carcinogenesis, its effects on the proliferation, apoptosis and cell cycle progression of MDA-MB-231 cells were examined using the lentiviral-mediated CENPU knockdown approach. The RNA and protein expression levels in the transfected cells were monitored using reverse transcription-quantitative polymerase chain reaction and western blotting, respectively. The mRNA and protein expression levels of the CENPU gene were significantly lower in the CENPU-shRNA transfected cells than in the control (P<0.01), indicating successful gene expression knockdown. Post-transfection, cell counting and MTT analysis revealed that the proliferation activity was significantly suppressed in CENPU knockdown cells relative to the control (P<0.01). Additionally, fluorescence activated cell sorting analysis revealed that the (G2+S) phase fraction was significantly declined in CENPU knockdown cells relative to the control; while the G1 phase fraction was significantly increased (P<0.01) and the percentage of the apoptotic cells was significantly increased (P<0.01). In conclusion, downregulation of CENPU gene expression may inhibit cell proliferation and cell cycle progression, and increase the apoptosis of the breast cancer cells. These results suggested a possible function of this protein in breast cancer pathogenesis and prognosis.

miRNA-21 and miRNA-223 expression signature as a predictor for lymph node metastasis, distant metastasis and survival in kidney renal clear cell carcinoma

Purpose: The aim of this study was to generate a novel miRNA expression signature to effectively assess nodal metastasis, distant metastasis and predict prognosis for patients with kidney renal clear cell carcinoma (KIRC) and explore its potential mechanism of affecting the prognosis. Method: Using expression profiles downloaded from the Cancer Genome Atlas database, we identified multiple miRNAs with differential expression between KIRC and paired normal tissues. The diagnostic values of the differentially expressed miRNAs for nodal metastasis and distant metastasis were evaluated by Receiver Operating Characteristic (ROC) curve analysis. Then, we evaluated the impact of miRNAs on overall survival (OS) by univariate and multivariate COX regression analyzes. This analysis was ultimately used to construct a miRNA signature that effectively assessed nodal metastasis, distant metastasis and predicted prognosis. The functional enrichment analysis of the miRNAs included in the signatures was used to explore its potential molecular mechanism in KIRC. Results: Based on our cutoff criteria (P < 0.05 and |log2FC| > 1.0), we identified 104 differentially expressed microRNAs (miRNAs), including 43 that were up-regulated in KIRC tissues and 61 that were down-regulated. We found 12 miRNAs were potentially diagnostic biomarkers of nodal metastasis and distant metastasis by ROC curve analysis. Two miRNAs (miRNA-21 and miRNA-223) were significant miRNAs independently associated with OS based on Cox univariate and multivariate analysis. We generated a signature index based on expression of these two miRNAs, and the two-miRNA signature is promising as a biomarker for diagnosing nodal metastasis, distant metastasis and predicting 5-year survival rate of KIRC with areas under the curve (AUC)=0.738, 0.659 and 0.731, respectively. Patients were stratified into high-risk and low-risk groups, according to median of the signature prognosis indexes. Patients in the high-risk group had significantly shorter survival times than those in the low-risk group (P = 0.000). The functional enrichment analysis suggested that the target genes of two miRNAs may be involved in various pathways related to cancer, p53 signaling pathway, apoptosis, and MAPK signaling pathway. Conclusion: The two-miRNA signature could assess nodal metastasis, distant metastasis and predict survival of KIRC. As a promising prediction tool, the mechanism of the two miRNAs in KIRC deserves further study.

Bioinformatic analysis of four miRNAs relevant to metastasis-regulated processes in endometrial carcinoma

BACKGROUND

The purpose of this study was to investigate the expression of different miRNAs in nonmetastatic and metastatic endometrial cancer Existing evidence indicates that there are many factors affecting the metastasis of endometrial cancer, and miRNAs play an unique role in many processes of endometiral cancer.

MATERIALS AND METHODS

miRNA sequences were downloaded from The Cancer Genome Atlas Project database, and Bioinformatics technique was used to deal with those data.

RESULTS

We elucidated the relation between differentially expressed miRNAs and clinical information for a total of 260 tumor tissues and 22 tumor tissues that had metastasized. We used the threshold of P <0.05| log 2 FC | >1.2 to identify potential miRNAs. Four differentially expressed miRNAs were identified in nonmetastatic and metastatic endometrial cancers. Further differential analysis of metastatic tissue revealed that miR-1247 is associated with metastasis of endometrial cancer to the lung, and miR-3200 is associated with the clinical stage of endometrial cancer. A functional enrichment analysis showed that the four miRNAs may be involved in multiple pathways of cancer, including the Wnt, NOTCH, and TGF-β signaling pathways and signaling pathways regulating pluripotency of stem cells. Protein-protein interaction analysis showed that PAK6, SNAP25, MAN1A1, MYB, ZBTB4, UST, ALDH1A3, and NRP2 are hub genes of relevant miRNAs in endometrial cancers.

CONCLUSION

The current study indicates that these four miRNAs may be related to molecular markers of metastasis of endometrial cancer.

Downregulation of miR‑486‑5p in papillary thyroid carcinoma tissue: A study based on microarray and miRNA sequencing

Abnormal expression of microRNA (miR) is associated with the occurrence and progression of various types of cancers, including papillary thyroid carcinoma (PTC). In the present study, the aim was to explore miR‑486‑5p expression and its role in PTC, as well as to investigate the biological function of its potential target genes. The expression levels of miR‑486‑5p and its clinicopathological significance were examined in 507 PTC and 59 normal thyroid samples via The Cancer Genome Atlas (TCGA). Subsequently, the results were validated using data from Gene Expression Omnibus (GEO) and ArrayExpress. Receiver operating characteristic and summary receiver operating characteristic curves were used to assess the ability of miR‑486‑5p in distinguishing PTC from normal tissue. Furthermore, potential miR‑486‑5p mRNA targets were identified using 12 prediction tools and enrichment analysis was performed on the encoding genes using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. The expression levels of miR‑486‑5p were consistently downregulated in PTC compared with in normal tissue across datasets from TCGA, GEO (GSE40807, GSE62054 and GSE73182) and ArrayExpress (E‑MTAB‑736). The results also demonstrated that miR‑486‑5p expression was associated with cancer stage (P=0.003), pathologic lymph node (P=0.047), metastasis (P=0.042), neoplasm (P=0.012) and recurrence (P=0.016) in patients with PTC. In addition, low expression of miR‑486‑5p in patients with PTC was associated with a worse overall survival. A total of 80 miR‑486‑5p‑related genes were observed from at least 9 of 12 prediction platforms, and these were involved in 'hsa05200: Pathways in cancer' and 'hsa05206: MicroRNAs in cancer'. Finally, three hub genes, CRK like proto‑oncogene, phosphatase and tensin homolog and tropomyosin 3, were identified as important candidates in tumorigenesis and progression of PTC. In conclusion, it may be hypothesized that miR‑486‑5p contributes towards PTC onset and progression, and may act as a clinical target. However, in vitro and in vivo experiments are required to validate the findings of the present study.

Oncogenic c-terminal cyclin D1 (CCND1) mutations are enriched in endometrioid endometrial adenocarcinomas

Cyclin D1 (CCND1) is a core cell cycle regulator and is frequently overexpressed in human cancers, often via amplification, translocation or post-transcription regulation. Accumulating evidence suggests that mutations of the CCND1 gene that result in nuclear retention and constitutive activation of CDK4/6 kinases are oncogenic drivers in cancer. However, the spectrum of CCND1 mutations across human cancers has not been systematically investigated. Here, we retrospectively mined whole-exome sequencing data from 124 published studies representing up to 29,432 cases from diverse cancer types and sites of origin, including carcinoma, melanoma, sarcoma and lymphoma/leukemia, via online tools to determine the frequency and spectrum of CCND1 mutations in human cancers and their associated clinico-pathological characteristics. Overall, in contrast to gene amplification, which occurred at a frequency of 4.8% (1,419 of 28,769 cases), CCND1 mutations were of very low frequency (0.5%, 151 of 29,432 cases) across all cancers, but were predominantly enriched in uterine endometrioid-type adenocarcinoma (6.5%, 30 of 458 cases) in both primary tumors and in advanced, metastatic endometrial cancer samples. CCND1 mutations in endometrial endometrioid adenocarcinoma occurred most commonly in the c-terminus of cyclin D1, as putative driver mutations, in a region thought to result in oncogenic activation of cyclin D1 via inhibition of Thr-286 phosphorylation and nuclear export, thereby resulting in nuclear retention and protein overexpression. Our findings implicate oncogenic c-terminal mutations of CCND1 in the pathogenesis of a subset of human cancers and provide a key resource to guide future preclinical and clinical investigations.

A Five-microRNA Signature for Survival Prognosis in Pancreatic Adenocarcinoma based on TCGA Data

Novel biomarkers for pancreatic adenocarcinoma are urgently needed because of its poor prognosis. Here, by using The Cancer Genome Atlas (TCGA) RNA-seq data, we evaluated the prognostic values of the differentially expressed miRNAs and constructed a five-miRNA signature that could effectively predict patient overall survival (OS). The Kaplan-Meier overall survival curves of two groups based on the five miRNAs were notably different, showing overall survival in 10.2% and 47.8% at five years for patients in high-risk and low-risk groups, respectively. The ROC curve analysis achieved AUC of 0.775, showing good sensitivity and specificity of the five-miRNA signature model in predicting pancreatic adenocarcinoma patient survival risk. The functional enrichment analysis suggested that the target genes of the miRNA signature may be involved in various pathways related to cancer, including PI3K-Akt, TGF-β, and pluripotent stem cell signaling pathways. Finally, we analyzed expression of the five specific miRNAs in the miRNA signature, and validated the reliability of the results in 20 newly diagnosed pancreatic adenocarcinoma patients using qRT-PCR. The expression results of qRT-PCR were consistent with the TCGA results. Taken together, these findings suggested that the five-miRNA signature (hsa-miR-203, hsa-miR-424, hsa-miR-1266 hsa-miR-1293, and hsa-miR-4772) could be used as a prognostic marker for pancreatic adenocarcinoma.

Four-miRNA signature as a prognostic tool for lung adenocarcinoma

Purpose

The aim of this study was to generate a novel miRNA expression signature to accurately predict prognosis for patients with lung adenocarcinoma (LUAD).

Patients and methods

Using expression profiles downloaded from The Cancer Genome Atlas database, we identified multiple miRNAs with differential expression between LUAD and paired healthy tissues. We then evaluated the prognostic values of the differentially expressed miRNAs using univariate/multivariate Cox regression analysis. This analysis was ultimately used to construct a four-miRNA signature that effectively predicted patient survival. Finally, we analyzed potential functional roles of the target genes for these four miRNAs using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses.

Results

Based on our cutoff criteria (P<0.05 and |log2FC| >1.0), we identified a total of 187 differentially expressed miRNAs, including 148 that were upregulated in LUAD tissues and 39 that were downregulated. Four miRNAs (miR-148a-5p, miR-31-5p, miR-548v, and miR-550a-5p) were independently associated with survival based on Kaplan–Meier analysis. We generated a signature index based on the expression of these four miRNAs and stratified patients into low- and high-risk groups. Patients in the high-risk group had significantly shorter survival times than those in the low-risk group (P=0.002). A functional enrichment analysis suggested that the target genes of these four miRNAs were involved in protein phosphorylation and the Hippo and sphingolipid signaling pathways.

Conclusion

Taken together, our results suggest that our four-miRNA signature can be used as a prognostic tool for patients with LUAD.

Discerning molecular interactions: A comprehensive review on biomolecular interaction databases and network analysis tools

Computational analysis of biomolecular interaction networks is now gaining a lot of importance to understand the functions of novel genes/proteins. Gene interaction (GI) network analysis and protein-protein interaction (PPI) network analysis play a major role in predicting the functionality of interacting genes or proteins and gives an insight into the functional relationships and evolutionary conservation of interactions among the genes. An interaction network is a graphical representation of gene/protein interactome, where each gene/protein is a node, and interaction between gene/protein is an edge. In this review, we discuss the popular open source databases that serve as data repositories to search and collect protein/gene interaction data, and also tools available for the generation of interaction network, visualization and network analysis. Also, various network analysis approaches like topological approach and clustering approach to study the network properties and functional enrichment server which illustrates the functions and pathway of the genes and proteins has been discussed. Hence the distinctive attribute mentioned in this review is not only to provide an overview of tools and web servers for gene and protein-protein interaction (PPI) network analysis but also to extract useful and meaningful information from the interaction networks.

A three miRNAs signature predicts survival in cervical cancer using bioinformatics analysis

Growing evidences showed that a large number of miRNAs were abnormally expressed in cervical cancer tissues and played irreplaceable roles in tumorigenesis, progression and metastasis. The aim of the present study was to identify the differential miRNAs expression between cervical cancer and normal cervical tissues by analyzing the high-throughput miRNA data downloaded from TCGA database. Additionally, we evaluated the prognostic values of the differentially expressed miRNAs and constructed a three-miRNA signature that could effectively predict patient survival. According to the cut-off criteria (P < 0.05 and |log₂FC| > 2.0), a total of 78 differentially expressed miRNAs were identified between cervical cancer tissues and matched normal tissues, including 37 up-regulated miRNAs and 41 down-regulated miRNAs. The Kaplan-Meier survival method revealed the prognostic function of the three miRNAs (miRNA-145, miRNA-200c, and miRNA-218-1). Univariate and multivariate Cox regression analysis showed that the three-miRNA signature was an independent prognostic factor in cervical cancer. The functional enrichment analysis suggested that the target genes of three miRNAs may be involved in various pathways related to cancer, including MAPK, AMPK, focal adhesion, cGMP-PKG, wnt, and mTOR signaling pathway. Taken together, the present study suggested that three-miRNA signature could be used as a prognostic marker in cervical cancer.