Developing a prognostic micro-RNA signature for human cervical carcinoma.
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ABSTRACT:
SUBMITTER: How C
PROVIDER: S-EPMC4399941 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The spectrum of nonalcoholic fatty liver disease (NAFLD) describes disease conditions deteriorating from nonalcoholic fatty liver (NAFL) to nonalcoholic steatohepatitis (NASH) to cirrhosis (CIR) to hepatocellular carcinoma (HCC). From a molecular and biochemical perspective, our understanding of the etiology of this disease is limited by the broad spectrum of disease presentations, the lack of a thorough understanding of the factors contributing to disease susceptibility, and ethical concerns related to repeat sampling of the liver. To better understand the factors associated with disease progression, we investigated by next-generation RNA sequencing the altered expression of microRNAs (miRNAs) in liver biopsies of class III obese subjects (body mass index ?40 kg/m(2)) biopsied at the time of elective bariatric surgery.Clinical characteristics and unbiased RNA expression profiles for 233 miRs, 313 transfer RNAs (tRNAs), and 392 miscellaneous small RNAs (snoRNAs, snRNAs, rRNAs) were compared among 36 liver biopsy specimens stratified by disease severity.The abundances of 3 miRNAs that were found to be differentially regulated (miR-301a-3p and miR-34a-5p increased and miR-375 decreased) with disease progression were validated by RT-PCR. No tRNAs or miscellaneous RNAs were found to be associated with disease severity. Similar patterns of increased miR-301a and decreased miR-375 expression were observed in 134 hepatocellular carcinoma (HCC) samples deposited in The Cancer Genome Atlas (TCGA).Our analytical results suggest that NAFLD severity is associated with a specific pattern of altered hepatic microRNA expression that may drive the hallmark of this disorder: altered lipid and carbohydrate metabolism. The three identified miRNAs can potentially be used as biomarkers to access the severity of NAFLD. The persistence of this miRNA expression pattern in an external validation cohort of HCC samples suggests that specific microRNA expression patterns may permit and/or sustain NAFLD development to HCC.
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Project description:Background: Cervical squamous cell carcinoma (CESC) is one of the most common causes of cancer-related death worldwide. N6-methyladenosine (m6A) plays an important role in various cellular responses by regulating mRNA biology. This study aimed to develop and validate an m6A RNA methylation regulator-based signature for prognostic prediction in CESC. Methods: Clinical and survival data as well as RNA sequencing data of 13 m6A RNA methylation regulators were obtained from The Cancer Genome Atlas (TCGA) CESC database. Consensus clustering was performed to identify different CESC clusters based on the differential expression of the regulators. LASSO Cox regression analysis was used to generate a prognostic signature based on m6A RNA methylation regulator expression. The effect of the signature was further explored by univariate and multivariate Cox analyses. Results: Four regulators (RBM15, METTL3, FTO, and YTHDF2) were identified to be aberrantly expressed in CESC tissues. A prognostic signature that includes ZC3H13, YTHDC1, and YTHDF1 was developed, which can act as an independent prognostic indicator. Significant differences of survival rate and clinicopathological features were found between the high- and low-risk groups. The results of bioinformatics analysis were then validated in the clinical CESC cohort by qRT-PCR and immunohistochemistry staining. Conclusion: In the present study, we developed and validated an m6A RNA methylation regulator-based prognostic signature, which might provide useful insights regarding the development and prognosis of CESC.
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Project description:Clinicopathological characteristics alone are not enough to predict the survival of patients with cervical squamous cell carcinoma (CESC) due to clinical heterogeneity. In recent years, many genes and non-coding RNAs have been shown to be oncogenes or tumor-suppressors in CESC cells. This study aimed to develop a comprehensive transcriptomic signature for CESC patient prognosis. Univariate, multivariate, and Least Absolute Shrinkage and Selection Operator penalized Cox regression were used to identify prognostic signatures for CESC patients from transcriptomic data of The Cancer Genome Atlas. A normalized prognostic index (NPI) was formulated as a synthetical index for CESC prognosis. Time-dependent receiver operating characteristic curve analysis was used to compare prognostic signatures. A prognostic transcriptomic signature was identified, including 1 microRNA, 1 long non-coding RNA, and 6 messenger RNAs. Decreased survival was associated with CESC patients being in the high-risk group stratified by NPI. The NPI was an independent predictor for CESC patient prognosis and it outperformed the known clinicopathological characteristics, microRNA-only signature, gene-only signature, and previously identified microRNA and gene signatures. Function and pathway enrichment analysis revealed that the identified prognostic RNAs were mainly involved in angiogenesis. In conclusion, we proposed a transcriptomic signature for CESC prognosis and it may be useful for effective clinical risk management of CESC patients. Moreover, RNAs in the transcriptomic signature provided clues for downstream experimental validation and mechanism exploration.
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Project description:BackgroundCervical cancer is frequently detected gynecological cancer all over the world. This study was designed to develop a prognostic signature for an effective prediction of cervical cancer prognosis.MethodsDifferentially expressed genes (DEGs) were identified based on copy number variation (CNV) data and expression profiles from different databases. A prognostic model was constructed and further optimized by stepwise Akaike information criterion (stepAIC). The model was then evaluated in three groups (training group, test group and validation group). Functional analysis and immune analysis were used to assess the difference between high-risk and low-risk groups.ResultsThe study developed a 5-gene prognostic model that could accurately classify cervical cancer samples into high-risk and low-risk groups with distinctly different prognosis. Low-risk group exhibited more favorable prognosis and higher immune infiltration than high-risk group. Both univariate and multivariate Cox regression analysis showed that the risk score was an independent risk factor for cervical cancer.ConclusionsThe 5-gene prognostic signature could serve as a predictor for identifying high-risk cervical cancer patients, and provided potential direction for studying the mechanism or drug targets of cervical cancer. The integrated analysis of CNV and mRNA expanded a new perspective for exploring prognostic signatures in cervical cancer.
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Project description:BackgroundIn this study, we aimed to investigate the signature of the autophagy-related lncRNAs (ARLs) and perform integrated analysis with immune infiltration in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC).Methods and resultsThe UCSC Xena and HADb databases provided the corresponding data. The ARLs were selected via constructing a co-expression network of autophagy-related genes (ARGs) and lncRNAs. Univariate Cox regression analysis combined with LASSO regression and multivariate Cox regression analysis were utilized to screen lncRNAs. The ARL risk signature was established by Cox regression and tested if it was an independent element bound up with patient prognosis. We used the xCell algorithm and ssGSEA to clarify the pertinence between immune infiltration and the expression of ARLs. Finally, we predicted the sensitivity of drug treatment as well as the immune response. Results indicated that the three prognostic ARLs (SMURF2P1, MIR9-3HG, and AC005332.4) possessed significant diversity and constituted the ARL signature. Risk score was an individual element (HR = 2.82, 95% CI = 1.87-4.30; p < 0.001). Immune infiltration analysis revealed significant increases in central memory CD8+ T cells, endothelial cells, CD8+ naive T cells, and preadipocytes in the high-risk group (p < 0.05). There were 10 therapeutic agents that varied significantly in their estimated half-maximal inhibitory concentrations in the two groups. According to the experimental validation, we found that SMURF2P1 belongs to the co-stimulatory genes and might assume greater importance in the development of cervical adenocarcinoma. MIR9-3HG and AC005332.4 belonged to the tumor-suppressor genes and they may play a more positive role in cervical squamous cell carcinoma.ConclusionsThis research explored and validated a novel signature of the ARLs, which can be applied to forecast the prognosis of patients with CESC and is closely associated with immune infiltration.
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Project description:Hepatocellular carcinoma (HCC) is a highly malignant and aggressive cancer with high recurrence rates and mortality. Some studies have illustrated that RNA binding proteins (RBPs) were involved in the carcinogenesis and development of multiple cancers, but the roles in HCC were still unclear. We downloaded the RNA-seq and corresponding clinical information of HCC from The Cancer Genome Atlas (TCGA) database, and 330 differentially expressed RBPs were identified between normal and HCC tissues. Through series of the univariate, the least absolute shrinkage selection operator (LASSO), and the stepwise multivariate Cox regression analyses, six prognosis-related key RBPs (CNOT6, UPF3B, MRPL54, ZC3H13, IFIT5, and PPARGC1A) were screened out from DE RBPs, and a six-RBP gene risk score signature was constructed in training set. Survival analysis indicated that HCC patients with high-risk scores had significantly worse overall survival than low-risk patients, and furthermore, the signature can be used as an independent prognostic indicator. The good accuracy of this prognostic signature was confirmed by the ROC curve analysis and was further validated in the International Cancer Genome Consortium (ICGC) HCC cohort. Besides, a nomogram based on six RBP genes was established and internally validated in the TCGA cohort. Gene set enrichment analysis demonstrated some cancer-related phenotypes were significantly gathered in the high-risk group. Overall, our study first identified an RBP-related six-gene prognostic signature, which could serve as a promising prognostic biomarker and provide some potential therapeutic targets for HCC.
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Project description:BackgroundGamma delta (γδ) T cells play dual roles in human tumors, with both antitumor and tumor-promoting functions. However, the role of γδT cells in HPV-infected cervical cancer is still undetermined. Therefore, we aimed to identify γδT cell-related prognostic signatures in the cervical tumor microenvironment.MethodsSingle-cell RNA-sequencing (scRNA-seq) data, bulk RNA-seq data, and corresponding clinical information of cervical cancer patients were obtained from the TCGA and GEO databases. The Seurat R package was used for single-cell analysis, and machine learning algorithms were used to screen and construct a γδT cell-related prognostic signature. Real-time quantitative PCR (RT-qPCR) was performed to detect the expression of prognostic signature genes.ResultsSingle-cell analysis indicated distinct populations of γδT cells between HPV-positive (HPV+) and HPV-negative (HPV-) cervical cancers. A trajectory analysis indicated γδT cells clustered into differential clusters with the pseudotime. High-dimensional Weighted Gene Co-expression Network Analysis (hdWGCNA) identified the key γδT cell-related gene modules. Bulk RNA-seq analysis also demonstrated the heterogeneity of immune cells, and the γδT-score was positively associated with inflammatory response and negatively associated with MYC stemness. Eight γδT cell-related hub genes (GTRGs), including ITGAE, IKZF3, LSP1, NEDD9, CLEC2D, RBPJ, TRBC2, and OXNAD1, were selected and validated as a prognostic signature for cervical cancer.ConclusionWe identified γδT cell-related prognostic signatures that can be considered independent factors for survival prediction in cervical cancer.
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Project description:Given the high morbidity and the trend of younger individuals being affected observed in cervical cancer, it is important to identify sensitive and effective biomarkers for predicting the survival outcome of patients. Based on data from 307 cervical cancer cases acquired from The Cancer Genome Atlas portal, 1920 differentially expressed mRNAs, 70 microRNAs(miRNAs), and 493 long non-coding(lncRNAs) were screened by comparing cervical cancer tissues with paracancerous tissues. A competing endogenous (ceRNA) network containing 50 lncRNAs, 16 miRNAs, and 81 mRNAs was constructed. Eighteen RNAs, comprising 13 mRNAs, 2 miRNAs, and 3 lncRNAs, were identified as significant prognostic factors by univariate Cox proportional hazards regression. ETS-related gene and fatty acid synthase signatures were discovered using a multivariate Cox regression model built to identify independent prognostic factors in cervical cancer patients. Receiver operating characteristic (ROC) analysis was used to determine the optimal cut-off value for distinguishing the risk level of cervical cancer patients. High-risk patients exhibited a poorer prognosis than low-risk patients did. This study focused on ceRNA networks to provide a novel perspective and insight into cervical cancer and suggested that the identified signature can serve as an independent prognostic biomarker in cervical cancer.
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Project description:Purpose: Our research developed immune-related long noncoding RNAs (lncRNAs) for risk stratification in cervical cancer (CC) and explored factors of prognosis, inflammatory microenvironment infiltrates, and chemotherapeutic therapies. Methods: The RNA-seq data and clinical information of CC were collected from the TCGA TARGET GTEx database and the TCGA database. lncRNAs and immune-related signatures were obtained from the GENCODE database and the ImPort database, respectively. We screened out immune-related lncRNA signatures through univariate Cox, LASSO, and multivariate Cox regression methods. We established an immune-related risk model of hub immune-related lncRNAs to evaluate whether the risk score was an independent prognostic predictor. The xCell and CIBERSORTx algorithms were employed to appraise the value of risk scores which are in competition with tumor-infiltrating immune cell abundances. The estimation of tumor immunotherapy response through the TIDE algorithm and prediction of innovative recommended medications on the target to immune-related risk model were also performed on the basis of the IC50 predictor. Results: We successfully established six immune-related lncRNAs (AC006126.4, EGFR-AS1, RP4-647J21.1, LINC00925, EMX2OS, and BZRAP1-AS1) to carry out prognostic prediction of CC. The immune-related risk model was constructed in which we observed that high-risk groups were strongly linked with poor survival outcomes. Risk scores varied with clinicopathological parameters and the tumor stage and were an independent hazard factor that affect prognosis of CC. The xCell algorithm revealed that hub immune-related signatures were relevant to immune cells, especially mast cells, DCs, megakaryocytes, memory B cells, NK cells, and Th1 cells. The CIBERSORTx algorithm revealed an inflammatory microenvironment where naive B cells (p < 0.01), activated dendritic cells (p < 0.05), activated mast cells (p < 0.0001), CD8+ T cells (p < 0.001), and regulatory T cells (p < 0.01) were significantly lower in the high-risk group, while macrophages M0 (p < 0.001), macrophages M2 (p < 0.05), resting mast cells (p < 0.0001), and neutrophils (p < 0.01) were highly conferred. The result of TIDE indicated that the number of immunotherapy responders in the low-risk group (124/137) increased significantly (p = 0.00000022) compared to the high-risk group (94/137), suggesting that the immunotherapy response of CC patients was completely negatively correlated with the risk scores. Last, we compared differential IC50 predictive values in high- and low-risk groups, and 12 compounds were identified as future treatments for CC patients. Conclusion: In this study, six immune-related lncRNAs were suggested to predict the outcome of CC, which is beneficial to the formulation of immunotherapy.
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