Identification of Tumorigenic and Prognostic Biomarkers in Colorectal Cancer Based on microRNA Expression Profiles

Objective. Although noncoding RNAs, especially the microRNAs, have been found to play key roles in CRC development in intestinal tissue, the specific mechanism of these microRNAs has not been fully understood. Methods. GEO and TCGA database were used to explore the microRNA expression profiles of normal mucosa, adenoma, and carcinoma. And the differential expression genes were selected. Computationally, we built the SVM model and multivariable Cox regression model to evaluate the performance of tumorigenic microRNAs in discriminating the adenomas from normal tissues and risk prediction. Results. In this study, we identified 20 miRNA biomarkers dysregulated in the colon adenomas. The functional enrichment analysis showed that MAPK activity and MAPK cascade were highly enriched by these tumorigenic microRNAs. We also investigated the target genes of the tumorigenic microRNAs. Eleven genes, including PIGF, TPI1, KLF4, RARS, PCBP2, EIF5A, HK2, RAVER2, HMGN1, MAPK6, and NDUFA2, were identified to be frequently targeted by the tumorigenic microRNAs. The high AUC value and distinct overall survival rates between the two risk groups suggested that these tumorigenic microRNAs had the potential of diagnostic and prognostic value in CRC. Conclusions. The present study revealed possible mechanisms and pathways that may contribute to tumorigenesis of CRC, which could not only be used as CRC early detection biomarkers, but also be useful for tumorigenesis mechanism studies.

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miRNA Mediated Noise Making of 3′UTR Mutations in Cancer

Genes ◽

10.3390/genes9110545 ◽

2018 ◽

Vol 9 (11) ◽

pp. 545 ◽

Cited By ~ 5

Author(s):

Wei Wu ◽

Lingxiang Wu ◽

Mengyan Zhu ◽

Ziyu Wang ◽

Min Wu ◽

...

Keyword(s):

Somatic Mutations ◽

Target Genes ◽

Expression Profiles ◽

Tumor Development ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Messenger Rnas ◽

Cellular Functions ◽

Mrna Binding

Somatic mutations in 3′-untranslated regions (3′UTR) do not alter amino acids and are considered to be silent in cancers. We found that such mutations can promote tumor progression by altering microRNA (miRNA) targeting efficiency and consequently affecting miRNA–mRNA interactions. We identified 67,159 somatic mutations located in the 3′UTRs of messenger RNAs (mRNAs) which can alter miRNA–mRNA interactions (functional somatic mutations, funcMutations), and 69.3% of these funcMutations (the degree of energy change > 12 kcal/mol) were identified to significantly promote loss of miRNA-mRNA binding. By integrating mRNA expression profiles of 21 cancer types, we found that the expression of target genes was positively correlated with the loss of absolute affinity level and negatively correlated with the gain of absolute affinity level. Functional enrichment analysis revealed that genes carrying funcMutations were significantly enriched in the MAPK and WNT signaling pathways, and analysis of regulatory modules identified eighteen miRNA modules involved with similar cellular functions. Our findings elucidate a complex relationship between miRNA, mRNA, and mutations, and suggest that 3′UTR mutations may play an important role in tumor development.

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

10.21203/rs.3.rs-27286/v1 ◽

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.

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Identification and Validation of a Potential Prognostic 7-lncRNA Signature for Predicting Survival in Patients with Multiple Myeloma

BioMed Research International ◽

10.1155/2020/3813546 ◽

2020 ◽

Vol 2020 ◽

pp. 1-16 ◽

Cited By ~ 1

Author(s):

Yun Zhong ◽

Zhe Liu ◽

Dangchi Li ◽

Qinyuan Liao ◽

Jingao Li

Keyword(s):

Gene Expression ◽

Risk Score ◽

Cox Regression ◽

External Validation ◽

Survival Rates ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Test Set ◽

Cox Regression Analysis

Background. An increasing number of studies have indicated that the abnormal expression of certain long noncoding RNAs (lncRNAs) is linked to the overall survival (OS) of patients with myeloma. Methods. Gene expression data of myeloma patients were downloaded from the Gene Expression Omnibus (GEO) database (GSE4581 and GSE57317). Cox regression analysis, Kaplan-Meier, and receiver operating characteristic (ROC) analysis were performed to construct and validate the prediction model. Single sample gene set enrichment (ssGSEA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used to predict the function of a specified lncRNA. Results. In this study, a seven-lncRNA signature was identified and used to construct a risk score system for myeloma prognosis. This system was used to stratify patients with different survival rates in the training set into high-risk and low-risk groups. Test set, the entire test set, the external validation set, and the myeloma subtype achieved the authentication of the results. In addition, functional enrichment analysis indicated that 7 prognostic lncRNAs may be involved in the tumorigenesis of myeloma through cancer-related pathways and biological processes. The results of the immune score showed that IF_I was negatively correlated with the risk score. Compared with the published gene signature, the 7-lncRNA model has a higher C-index (above 0.8). Conclusion. In summary, our data provide evidence that seven lncRNAs could be used as independent biomarkers to predict the prognosis of myeloma, which also indicated that these 7 lncRNAs may be involved in the progression of myeloma.

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Six-long Non-coding RNA Signature to Predict the Prognosis of Lung Adenocarcinoma

10.21203/rs.3.rs-56767/v1 ◽

2020 ◽

Author(s):

Yang Wang ◽

Chengping Hu

Keyword(s):

Lung Adenocarcinoma ◽

Cox Regression ◽

Survival Rates ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Protein Coding ◽

Cox Regression Analysis ◽

Protein Coding Genes

Abstract Background: Long non-coding RNAs (lncRNAs) have been reported to play essential roles in tumorigenesis and cancers prognosis, and they can be a potential cancer prognostic markers. However, in lung adenocarcinoma(LUAD), how lncRNA signatures predict the survival of patients is poorly understood. Our study aims to explore lncRNA signatures and prognostic function in LUAD.Methods: The expression and prognosis data of lncRNAs in LUAD patients was collected from the Cancer Genome Atlas (TCGA) data. All analyses were performed using the R package (version 3.6.2). Metascape, STRING and Cytoscape were used for enrichment analysis and function prediction of the lncRNA co-expressed protein-coding genes.Results: We have collected lncRNA expression data in 466 LUAD tumors, and a six-lncRNA signature(RP11-79H23.3, RP11-309M7.1, CTD-2357A8.3, RP11-108P20.4, U47924.29, LHFPL3-AS2) has been shown to be significantly related to LUAD patients’ overall survival. According to the lncRNA signatures, the high-risk and low-risk groups were divided in LUAD patients with different survival rates. Further multivariable cox regression analysis showed that the prognostic value of this signature was independent of clinical factors. The potential functional roles and hub co-expressed protein-coding genes in the six prognostic lncRNAs are shown in the functional enrichment analysis.Conclusions: These results showed that these six lncRNAs could be independent predicted prognostic biomarkers in LUAD patients.

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

Scientific Reports ◽

10.1038/s41598-020-79337-4 ◽

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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A novel seven-microRNA signature predicts prognosis in colorectal adenocarcinoma

10.21203/rs.3.rs-46147/v1 ◽

2020 ◽

Author(s):

Shengying Jiang ◽

Xiaoli Xie ◽

Hui-Qing Jiang

Keyword(s):

Colorectal Adenocarcinoma ◽

Cox Regression ◽

Expression Profiles ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Mirna Signature ◽

Training Set ◽

Test Set ◽

Related Cell

Abstract Background Colorectal carcinoma, primarily colorectal adenocarcinoma (CRA), is one of the most common malignancies, ranking third, while contributing the second cause of cancer death in the world. MicroRNAs, a type of non-coding RNA, play an important role in regulating cancer-related cell biology. To simply and accurately predict survival risk for CRA patients, we identified a novel seven-miRNA signature. The microRNA (miRNA) expression profiles along with clinical data of 512 CRA patients were downloaded from The Cancer Genome Atlas (TCGA) database, and 415 patients with complete clinical information were further divided into training set and test set randomly. To construct the prognostic signature in the training set, a series of Cox regression analyses were performed, including univariate regression, least absolute shrinkage and selectionator operator (LASSO) - Cox regression, and multivariate regression. Results Seven predictive miRNAs (miR-153-2, miR-3199-2, miR-144, miR-887, miR-561, miR-3684, and miR-505) were ultimately screened. The ROC curves for 5-year survival in the training set, test set and entire set were 0.889, 0.742, and 0.816, respectively. Kaplan-Meier analysis results of the three sets all showed p <0.05. Further analyses demonstrated that the signature was an independent prognostic risk factor for CRA patients, and its predictive ability was superior to age and TNM stage. Functional enrichment analysis revealed that p53 and ErbB pathways were related to the prognostic regulation of miRNAs in the signature in CRA patients.Conclusion Our study demonstrated the potential of this novel seven-miRNA signature to independently predict overall survival in patients with CRA. Functional enrichment analysis further revealed the possible regulatory role of miRNAs in the signature in CRA-related cell biological processes and signaling pathways.

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Identification and characterization of Piwi-interacting RNAs in human placentas of preeclampsia

Scientific Reports ◽

10.1038/s41598-021-95307-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Jie He ◽

Miaomiao Chen ◽

Jiacheng Xu ◽

Jie Fang ◽

Zheng Liu ◽

...

Keyword(s):

Expression Pattern ◽

Human Placenta ◽

Target Genes ◽

Expression Profiles ◽

Differential Expression Analysis ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Small Rna Sequencing ◽

Common Disease

AbstractPreeclampsia is a common disease of pregnancy that poses a serious threat to the safety of pregnant women and the fetus; however, the etiology of preeclampsia is inconclusive. Piwi-interacting RNAs (piRNAs) are novel non-coding RNAs that are present at high levels in germ cells and are associated with spermatogenesis. Emerging evidence demonstrated that piRNA is expressed in a variety of human tissues and is closely associated with tumorigenesis. However, changes in the piRNA expression profile in the placenta have not been investigated. In this study, we used small RNA sequencing to evaluate the differences in piRNA expression profiles between preeclampsia and control patients and potential functions. Differential expression analysis found 41 up-regulated and 36 down-regulated piRNAs in preeclamptic samples. In addition, the functional enrichment analysis of piRNAs target genes indicated that they were related to the extracellular matrix (ECM) formation and tissue-specific. Finally, we examined the expression pattern of the PIWL family proteins in the placenta, and PIWL3 and PIWIL4 were the primary subtypes in the human placenta. In summary, this study first summarized the changes in the expression pattern of piRNA in preeclampsia and provided new clues for the regulatory role of piRNA in the human placenta.

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Understanding the Modus Operandi of MicroRNA Regulatory Clusters

Cells ◽

10.3390/cells8091103 ◽

2019 ◽

Vol 8 (9) ◽

pp. 1103 ◽

Cited By ~ 3

Author(s):

Arthur C. Oliveira ◽

Luiz A. Bovolenta ◽

Lucas Alves ◽

Lucas Figueiredo ◽

Amanda O. Ribeiro ◽

...

Keyword(s):

Gene Expression ◽

Target Genes ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Target Prediction ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Modus Operandi ◽

Wide Range

MicroRNAs (miRNAs) are non-coding RNAs that regulate a wide range of biological pathways by post-transcriptionally modulating gene expression levels. Given that even a single miRNA may simultaneously control several genes enrolled in multiple biological functions, one would expect that these tiny RNAs have the ability to properly sort among distinctive cellular processes to drive protein production. To test this hypothesis, we scrutinized previously published microarray datasets and clustered protein-coding gene expression profiles according to the intensity of fold-change levels caused by the exogenous transfection of 10 miRNAs (miR-1, miR-7, miR-9, miR-124, miR-128a, miR-132, miR-133a, miR-142, miR-148b, miR-181a) in a human cell line. Through an in silico functional enrichment analysis, we discovered non-randomic regulatory patterns, proper of each cluster identified. We demonstrated that miRNAs are capable of equivalently modulate the expression signatures of target genes in regulatory clusters according to the biological function they are assigned to. Moreover, target prediction analysis applied to ten vertebrate species, suggest that such miRNA regulatory modus operandi is evolutionarily conserved within vertebrates. Overall, we discovered a complex regulatory cluster-module strategy driven by miRNAs, which relies on the controlled intensity of the repression over distinct targets under specific biological contexts. Our discovery helps to clarify the mechanisms underlying the functional activity of miRNAs and makes it easier to take the fastest and most accurate path in the search for the functions of miRNAs in any distinct biological process of interest.

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Identification of Key Genes and Evaluation of Their Prognosis Potential in Hepatocellular Carcinoma by Integrated Bioinformatics Analysis

10.21203/rs.3.rs-40788/v1 ◽

2020 ◽

Author(s):

Jankun Liu ◽

zy liu ◽

Wei Li ◽

Xinghua Pan ◽

Zongjiang Fan ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Target Genes ◽

Bioinformatics Analysis ◽

Expression Profiles ◽

Survival Curve ◽

Treatment Strategies ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Key Genes

Abstract Background Hepatocellular carcinoma (HCC) is a malignancy causing highly death rate in the world. Despite the development of treatment strategies for HCC, prognosis of this malignancy remains unsatisfactory. In this study, we aimed to identify the target genes associated with the prognosis of HCC patients. Methods Three expression profiles of HCC tissues were extracted from the Gene Expression Omnibus database to explore the differentially expressed genes (DEGs) using GEO2R method. Functional enrichment analysis was performed to reveal the biological characteristics of DEGs. Protein-protein interaction (PPI) network was constructed using Cytoscape software. The survival curve of identified DEGs were tested by Kaplan-Meier analysis. Results We identified 15 DEGs (CYP39A1, CYR61, FOS, FOXO1, GADD45B, ID1, IL1RAP, MT1M, PHLDA1, RND3, SDS, SOCS2, TAT, S100P, and SPINK1) in HCC tissues. Prognosis analysis showed that 4 DEGs (FOXO1,SPINK1༌SOCS2, and TAT) correlated with overall survival time of HCC patients, which might serve as therapeutic targets for HCC patients. Conclusions By integrated bioinformatics analysis, we proposed a novel way to reveal key genes that closely relate to HCC development.

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

10.21203/rs.2.24062/v1 ◽

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.

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