scholarly journals Immunogenomic Landscape Analysis of Prognostic Immune-Related Genes in Hepatocellular Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Peng Qin ◽  
Mengyu Zhang ◽  
Xue Liu ◽  
Ziming Dong
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Receptor Interaction ◽  
Immune Related Genes

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death. HBV infection is an important risk factor for the tumorigenesis of HCC, given that the inflammatory environment is closely related to morbidity and prognosis. Consequently, it is of urgent importance to explore the immunogenomic landscape to supplement the prognosis of HCC. The expression profiles of immune‐related genes (IRGs) were integrated with 377 HCC patients to generate differentially expressed IRGs based on the Cancer Genome Atlas (TCGA) dataset. These IRGs were evaluated and assessed in terms of their diagnostic and prognostic values. A total of 32 differentially expressed immune‐related genes resulted as significantly correlated with the overall survival of HCC patients. The Gene Ontology functional enrichment analysis revealed that these genes were actively involved in cytokine‐cytokine receptor interaction. A prognostic signature based on IRGs (HSPA4, PSME3, PSMD14, FABP6, ISG20L2, TRAF3, NDRG1, NRAS, CSPG5, and IL17D) stratified patients into high-risk versus low-risk groups in terms of overall survival and remained as an independent prognostic factor in multivariate analyses after adjusting for clinical and pathologic factors. Several IRGs (HSPA4, PSME3, PSMD14, FABP6, ISG20L2, TRAF3, NDRG1, NRAS, CSPG5, and IL17D) of clinical significance were screened in the present study, revealing that the proposed clinical-immune signature is a promising risk score for predicting the prognosis of HCC.

scholarly journals Identification of a prognostic long non-coding RNA signature in breast cancer

2020 ◽  
Author(s):  
Gaochen Lan ◽  
Xiaoling Yu ◽  
Yanna Zhao ◽  
Jinjian Lan ◽  
Wan Li ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cox Regression ◽  
Prognostic Biomarker ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Cox Regression Analysis

Abstract Background: Breast cancer is the most common malignant disease among women. At present, more and more attention has been paid to long non-coding RNAs (lncRNAs) in the field of breast cancer research. We aimed to investigate the expression profiles of lncRNAs and construct a prognostic lncRNA for predicting the overall survival (OS) of breast cancer.Methods: The expression profiles of lncRNAs and clinical data with breast cancer were obtained from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs were screened out by R package (limma). The survival probability was estimated by the Kaplan‑Meier Test. The Cox Regression Model was performed for univariate and multivariate analysis. The risk score (RS) was established on the basis of the lncRNAs’ expression level (exp) multiplied regression coefficient (β) from the multivariate cox regression analysis with the following formula: RS=exp a1 * β a1 + exp a2 * β a2 +……+ exp an * β an. Functional enrichment analysis was performed by Metascape.Results: A total of 3404 differentially expressed lncRNAs were identified. Among them, CYTOR, MIR4458HG and MAPT-AS1 were significantly associated with the survival of breast cancer. Finally, The RS could predict OS of breast cancer (RS=exp CYTOR * β CYTOR + exp MIR4458HG * β MIR4458HG + exp MAPT-AS1 * β MAPT-AS1). Moreover, it was confirmed that the three-lncRNA signature could be an independent prognostic biomarker for breast cancer (HR=3.040, P=0.000).Conclusions: This study established a three-lncRNA signature, which might be a novel prognostic biomarker for breast cancer.

scholarly journals Comparative Transcriptome Analysis Suggests Key Roles for 5-Hydroxytryptamlne Receptors in Control of Goose Egg Production

Genes ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 455 ◽  
Author(s):  
Qingyuan Ouyang ◽  
Shenqiang Hu ◽  
Guosong Wang ◽  
Jiwei Hu ◽  
Jiaman Zhang ◽  
...  
Keyword(s):  
Egg Production ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Production Performance ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Receptor Interaction ◽  
Anser Anser

To date, research on poultry egg production performance has only been conducted within inter or intra-breed groups, while those combining both inter- and intra-breed groups are lacking. Egg production performance is known to differ markedly between Sichuan white goose (Anser cygnoides) and Landes goose (Anser anser). In order to understand the mechanism of egg production performance in geese, we undertook this study. Here, 18 ovarian stromal samples from both Sichuan white goose and Landes goose at the age of 145 days (3 individuals before egg production initiation for each breed) and 730 days (3 high- and low egg production individuals during non-laying periods for each breed) were collected to reveal the genome-wide expression profiles of ovarian mRNAs and lncRNAs between these two geese breeds at different physiological stages. Briefly, 58, 347, 797, 777, and 881 differentially expressed genes (DEGs) and 56, 24, 154, 105, and 224 differentially expressed long non-coding RNAs (DElncRNAs) were found in LLD vs. HLD (low egg production Landes goose vs. high egg production Landes goose), LSC vs. HSC (low egg production Sichuan White goose vs. high egg production Sichuan white goose), YLD vs. YSC (young Landes goose vs. young Sichuan white goose), HLD vs. HSC (high egg production Landes goose vs. high egg production Sichuan white goose), and LLD vs. LSC (low egg production Landes goose vs. low egg production Sichuan white goose) groups, respectively. Functional enrichment analysis of these DEGs and DElncRNAs suggest that the “neuroactive ligand–receptor interaction pathway” is crucial for egg production, and particularly, members of the 5-hydroxytryptamine receptor (HTR) family affect egg production by regulating ovarian metabolic function. Furthermore, the big differences in the secondary structures among HTR1F and HTR1B, HTR2B, and HTR7 may lead to their different expression patterns in goose ovaries of both inter- and intra-breed groups. These results provide novel insights into the mechanisms regulating poultry egg production performance.

scholarly journals Tumor-Infiltrating Leukocyte Compositions and Prognostic Immune-Related Genes in Pancreatic Cancer.

2020 ◽  
Author(s):  
Buwei Teng ◽  
Yuhan Yang ◽  
Zengya Guo ◽  
Kundong Zhang ◽  
Xiaofeng Wang ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
T Cells ◽  
Cd4 T Cells ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Immune Related Genes

Abstract Background:Pancreatic cancer (PC) is one of the most common cancers，which has poor prognosis.At present, abundant genetic PC samples can be obtained from The Cancer Genome Atlas (TCGA) database to finish comprehensive and reliable immunogenomic analysis. Thus, there is an urgent need to systematically explore the immunogenome of PC to obtain good prognosis.Methods: In this study, according to TCGA and The Genotype-Tissue Expression (GTEx) databases, we investigated the different compositions of leukocytes between PC and normal pancreas tissues, and analyzed the expressions of immune-related genes (IRGs) and the overall survival (OS) of 178 PC patients. Subsequently, computational difference algorithm and COX regression analyses were employed to assess the differentially expressed and OS-related IRGs in PC patients. Moreover, the underlying action mechanisms and properties of these IRGs were investigated by using computational biology. Finally, multivariable COX analysis was used to develop a novel prognostic biomarker for PC according to these IRGs.Results:The results showed that CD4+ memory T cells and M0 macrophages were more common and highly dominated in PC tissues relative to the non-tumor tissues. Functional enrichment analysis demonstrated that the differentially expressed and OS‐related IRGs were actively involved in the PI3K-Akt signaling pathway. A prognostic signature according to these differentially expressed IRGs (CD2AP, IL20RB, MYEOV, NUSAP1, PCDH1, RAB27B, TNFSF10, TOP2A, TPX2, TYK2, WNT7A and BUB1B) was moderately used for prognostic predictions. Further study indicated that RAB27B was negatively related to CD4 T cells while TYK2 was positively correlated with CD4 T cells. Conclusions: Taken together, this study screened several significant IRGs, demonstrated the drivers of immune repertoire, and indicated the importance of these PC-specific IRGs in the prognosis of PC.

Construction of immune-related risk model for prognosis of hepatocellular carcinoma.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e16683-e16683
Author(s):  
Yue Li ◽  
Ximing Xu
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Risk Score ◽  
Risk Model ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Related Risk ◽  
Immune Related Genes

e16683 Background: Hepatocellular carcinoma is the most common malignant tumor. Although the treatment of HCC has significantly improved, the 5-year survival rate is still only 18%. There is increasing evidence that tumor immune microenvironment (TIM) plays critical roles during cancer initiation and progression. Based on the comprehensive exploration of the immunogenomic, an immune-related risk model was constructed to predict hepatocellular carcinoma prognosis. Methods: Transcriptomic data of HCC patients were downloaded from the TCGA database, and the differentially expressed immune-related genes (IRGs) (FDR < 0.01, |log2fold change| > 2) were identified. Functional enrichment analysis was performed to explore potential molecular mechanisms of the differentially expressed IRGs. By univariate and multivariate Cox regression analysis, we identified eight prognosis-related IRGs. Based on the expression levels of IRGs, we constructed the immune-related risk model. The Kaplan‐Meier (K‐M) survival curves, ROC curves, univariate and multivariate analysis were used to evaluate the immune-related risk model. According to the risk score, HCC patients were stratified into low and high-risk groups. CIBERSORT was applied to analyze the profiling difference of infiltrating immune cells between the two groups. Results: A total of 113 differentially expressed IRGs were identified, of which nine IRGs were correlated with the prognosis of HCC patients. Functional enrichment analysis showed that these genes were involved in immune response and immune signal pathway. The immune-related risk model consisted of eight IRGs (FABP6, RBP2, MAPT, BIRC5, PLXNA3, CSPG5, IL17D and STC2). The immune risk score was an independent prognostic factor (HR, 2.63 [1.93−3.58]; P = 8.16E−10) and the patients with a high-risk score tended to have a shorter OS than those with a low-risk score. In the TCGA cohort, high-risk patients tended to have an advanced stage. Moreover, we found that the patients in the high-risk group had higher fractions of T follicular cells helper and macrophages M0. The patients with low-risk scores had higher fractions of CD8+ T cells and CD4+ T cells. Conclusions: We have identified the immune-related risk model of hepatocellular carcinoma based on the expression profiles of eight immune-related genes. This model could predict prognosis and reflect the tumor immune microenvironment of HCC patients, which can provide new insights in the individualized treatment of HCC and potential novel targets for immunotherapy.

scholarly journals Identification of Ten-LncRNA Signatures Predict Disease-Free and Overall Survival for Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Jiong Lu ◽  
Sishu Yang ◽  
xianze xiong
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Mapk Signaling ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Pathway Enrichment Analysis ◽  
Operating Characteristics ◽  
Disease Free

Abstract Background: The prognosis of hepatocellular carcinoma (HCC) is bleak though it has been improved over recent years. Early diagnosis could improve the survival. Plenty of researches indicate that long non-coding RNAs (lncRNAs) could play an important role in prognostic prediction of cancer as a kind of biomarker. Methods: We downloaded clinicopathological characteristics and lncRNA expression data of HCC patients from The Cancer Genome Atlas (TCGA) database. The ratio of training sets to validation sets was 2:1. Significant differentially expressed lncRNAs were identified by log-rank test and cox regression. All the significant lncRNAs were selected into the least absolute shrinkage and selection operator regression (LASSO) analysis and constructed risk-score formula by linear combination. Performance of the signatures were validated by receiver operating characteristics (ROC) curves and Kaplan-Meier survival curves. The correlated messenger RNAs (mRNA) were evaluated by functional enrichment analysis. Results: We identified and validated ten-lncRNAs based signatures to predict disease-free survival (DFS) and overall survival (OS) of HCC respectively. Stratified survival analysis showed that the performance of lncRNAs related signatures was better than tumor, node, metastasis(TNM) staging system. Functional enrichment analysis showed that organelle fission and regulation of mRNA metabolic process were significantly enriched in differentially expressed lncRNAs (DElncRNAs). Transcriptional misregulation in cancer and mitogen-activated protein kinase (MAPK) signaling pathway were significantly enriched pathways in the pathway enrichment analysis. Conclusion: we constructed two lncRNAs based signatures which could predict prognosis of HCC more accurate than the traditional ways.

scholarly journals Identification of a prognostic long non-coding RNA signature in breast cancer

2020 ◽  
Author(s):  
Gaochen Lan ◽  
Xiaoling Yu ◽  
Yanna Zhao ◽  
Jinjian Lan ◽  
Wan Li ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cox Regression ◽  
Prognostic Biomarker ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Cox Regression Analysis

Abstract Background: Breast cancer is the most common malignant disease among women. At present, more and more attention has been paid to long non-coding RNAs (lncRNAs) in the field of breast cancer research. We aimed to investigate the expression profiles of lncRNAs and construct a prognostic lncRNA for predicting the overall survival (OS) of breast cancer. Methods: The expression profiles of lncRNAs and clinical data with breast cancer were obtained from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs were screened out by R package (limma). The survival probability was estimated by the Kaplan‑Meier Test. The Cox Regression Model was performed for univariate and multivariate analysis. The risk score (RS) was established on the basis of the lncRNAs’ expression level (exp) multiplied regression coefficient (β) from the multivariate cox regression analysis with the following formula: RS=exp a1 * β a1 + exp a2 * β a2 +……+ exp an * β an . Functional enrichment analysis was performed by Metascape. Results: A total of 3404 differentially expressed lncRNAs were identified. Among them, CYTOR , MIR4458HG and MAPT-AS1 were significantly associated with the survival of breast cancer. Finally, The RS could predict OS of breast cancer (RS=exp CYTOR * β CYTOR + exp MIR4458HG * β MIR4458HG + exp MAPT-AS1 * β MAPT-AS1 ). Moreover, it was confirmed that the three-lncRNA signature could be an independent prognostic biomarker for breast cancer (HR=3.040, P=0.000). Conclusions: This study established a three-lncRNA signature, which might be a novel prognostic biomarker for breast cancer.

scholarly journals Identifcation of The Ferroptosis-Related Gene Signature In Placenta of Patients With Early-Onset Preeclampsia

2021 ◽  
Author(s):  
Nana Yang ◽  
Qianghua Wang ◽  
Biao Ding ◽  
Yinging Gong ◽  
Yue Wu ◽  
...  
Keyword(s):  
Iron Homeostasis ◽  
Molecular Mechanisms ◽  
Late Onset ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Ppi Network ◽  
Network Analyses

Abstract Background: The accumulation of ROS resulting from upregulated levels of oxidative stress is commonly implicated in preeclampsia (PE). Ferroptosis is a novel form of iron-dependent cell death instigated by lipid peroxidation likely plays important role in PE pathogenesis. This study aims to investigate expression profiles and functions of the ferroptosis-related genes (FRGs) in early- and late-onset preeclampsia.Methods: The gene expression data and clinical information were downloaded from GEO database. The “limma” R package was used for screening differentially expressed genes. GO(Gene Ontology), Kyoto Encyclopedia of Genes and Genomes(KEGG) and protein protein interaction (PPI) network analyses were conducted to investigate the bioinformatics functions and molecular interactions of significantly different FRGs. Quantitative real-time reverse transcriptase PCR was used to verify the expression of hub FRGs in PE.Results: A total number of 4,215 DEGs were identified between EOPE and preterm cases and 3,356 DEGs were found between EOPE and LOPE subtypes. 20 significantly different FRGs were identified in EOPE, while only 3 in LOPE. Functional enrichment analysis revealed that the differentially expressed FRGs was mainly involved in EOPE and enriched in hypoxia- and iron-related pathways, such as response to hypoxia, iron homeostasis and iron ion binding process. The PPI network analysis and verification by RT-qPCR resulted in the identification of the following six interesting FRGs: FTH1, HIF1A, FTL, IREB2, MAPK8 and PLIN2. Conclusions: EOPE and LOPE owned distinct underlying molecular mechanisms and ferroptosis may be mainly implicated in pathogenesis of EOPE. Further studies are necessary for deeper inquiry into placental ferroptosis and its role in the pathogenesis of EOPE.

scholarly journals Comparative transcriptomic analysis of porcine peripheral blood reveals differentially expressed genes from the cytokine–cytokine receptor interaction pathway related to health status

Genome ◽  
2017 ◽  
Vol 60 (12) ◽  
pp. 1021-1028 ◽  
Author(s):  
M.H. Ye ◽  
H. Bao ◽  
Y. Meng ◽  
L.L. Guan ◽  
P. Stothard ◽  
...  
Keyword(s):  
Health Status ◽  
Differentially Expressed Genes ◽  
Peripheral Blood ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Cytokine Receptor ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Receptor Interaction ◽  
Marker Genes

While some research has looked into the host genetic response in pigs challenged with specific viruses or bacteria, few studies have explored the expression changes of transcripts in the peripheral blood of sick pigs that may be infected with multiple pathogens on farms. In this study, the architecture of the peripheral blood transcriptome of 64 Duroc sired commercial pigs, including 18 healthy animals at entry to a growing facility (set as a control) and 23 pairs of samples from healthy and sick pen mates, was generated using RNA-Seq technology. In total, 246 differentially expressed genes were identified to be specific to the sick animals. Functional enrichment analysis for those genes revealed that the over-represented gene ontology terms for the biological processes category were exclusively immune activity related. The cytokine–cytokine receptor interaction pathway was significantly enriched. Nine functional genes from this pathway encoding members (as well as their receptors) of the interleukins, chemokines, tumor necrosis factors, colony stimulating factors, activins, and interferons exhibited significant transcriptional alteration in sick animals. Our results suggest a subset of novel marker genes that may be useful candidate genes in the evaluation and prediction of health status in pigs under commercial production conditions.

scholarly journals Construction of circRNA-Based ceRNA Network to Reveal the Role of circRNAs in the Progression and Prognosis of Hepatocellular Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Rong Deng ◽  
Xiaohan Cui ◽  
Yuxiang Dong ◽  
Yanqiu Tang ◽  
Xuewen Tao ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Cancer ◽  
Differential Expression Analysis ◽  
Reduction Process ◽  
Enrichment Analysis ◽  
Clinical Information ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Mirna Sequence

BackgroundCircular RNAs (circRNAs) are now under hot discussion as novel promising biomarkers for patients with hepatocellular carcinoma (HCC). The purpose of our study is to identify several competing endogenous RNA (ceRNA) networks related to the prognosis and progression of HCC and to further investigate the mechanism of their influence on tumor progression.MethodsFirst, we obtained gene expression data related to liver cancer from The Cancer Genome Atlas (TCGA) database (http://www.portal.gdc.cancer.gov/), including microRNA (miRNA) sequence, RNA sequence, and clinical information. A co-expression network was constructed through the Weighted Correlation Network Analysis (WGCNA) software package in R software. The differentially expressed messenger RNAs (DEmRNAs) in the key module were analyzed with the Database for Annotation Visualization and Integrated Discovery (DAVID) (https://david.ncifcrf.gov/summary.jsp) to perform functional enrichment analysis including Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). The data of miRNA expression and clinical information downloaded from TCGA were utilized for survival analysis to detach the prognostic value of the DEmiRNAs of the key module.ResultsThe 201 differentially expressed miRNAs (DEmiRNAs) and 3,783 DEmRNAs were preliminarily identified through differential expression analysis. The co-expression networks of DEmiRNAs and DEmRNAs were constructed with WGCNA. Further analysis confirmed four miRNAs in the most significant module (blue module) were associated with the overall survival (OS) of patients with liver cancer, including hsa-miR-92b-3p, hsa-miR-122-3p, hsa-miR-139-5p, and hsa-miR-7850-5p. DAVID was used for functional enrichment analysis of 286 co-expressed mRNAs. The GO analysis results showed that the top enriched GO terms were oxidation–reduction process, extracellular exosome, and iron ion binding. In KEGG pathway analysis, the top three enriched terms included metabolic pathways, fatty acid degradation, and valine, leucine, and isoleucine degradation. In addition, we intersected the miRNA–mRNA interaction prediction results with the differentially expressed and prognostic mRNAs. We found that hsa-miR-92b-3p can be related to CPEB3 and ACADL. By overlapping the data of predicted circRNAs by circBank and differentially expressed circRNAs of GSE94508, we screened has_circ_0077210 as the upstream regulatory molecule of hsa-miR-92b-3p. Hsa_circ_0077210/hsa-miR-92b-3p/cytoplasmic polyadenylation element binding protein-3 (CPEB3) and acyl-Coenzyme A dehydrogenase, long chain (ACADL) were validated in HCC tissue.ConclusionOur research provides a mechanistic elucidation of the unknown ceRNA regulatory network in HCC. Hsa_circ_0077210 might serve a momentous therapeutic role to restrain the occurrence and development of HCC.

scholarly journals Identification and validation of a miRNA-based prognostic signature for cervical cancer through an integrated bioinformatics approach

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yumei Qi ◽  
Yo-Liang Lai ◽  
Pei-Chun Shen ◽  
Fang-Hsin Chen ◽  
Li-Jie Lin ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Poor Prognosis ◽  
Expression Profiles ◽  
Survival Rates ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Additive Effects

AbstractCervical cancer is the fourth most common cancer in women worldwide. Increasing evidence has shown that miRNAs are related to the progression of cervical cancer. However, the mechanisms that affect the prognosis of cancer are still largely unknown. In the present study, we sought to identify miRNAs associated with poor prognosis of patient with cervical cancer, as well as the possible mechanisms regulated by them. The miRNA expression profiles and relevant clinical information of patients with cervical cancer were obtained from The Cancer Genome Atlas (TCGA). The selection of prognostic miRNAs was carried out through an integrated bioinformatics approach. The most effective miRNAs with synergistic and additive effects were selected for validation through in vitro experiments. Three miRNAs (miR-216b-5p, miR-585-5p, and miR-7641) were identified as exhibiting good performance in predicting poor prognosis through additive effects analysis. The functional enrichment analysis suggested that not only pathways traditionally involved in cancer but also immune system pathways might be important in regulating the outcome of the disease. Our findings demonstrated that a synergistic combination of three miRNAs may be associated, through their regulation of specific pathways, with very poor survival rates for patients with cervical cancer.

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