Transcriptomic Analysis of the Aquaporin Gene Family and Associated Interactors in Rectal Cancer

MicroRNA ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 153-166 ◽  
Author(s):  
Dimitrios E. Magouliotis ◽  
Vasiliki S. Tasiopoulou ◽  
Ioannis Baloyiannis ◽  
Ioannis Mamaloudis ◽  
George Tzovaras
Keyword(s):  
Rectal Cancer ◽  
Gene Family ◽  
Survival Data ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Tissue Samples ◽  
Aquaporin Gene ◽  
Deming Regression

Background: Rectal Cancer (RC) is a common type of cancer with poor prognosis. The identification of biomarkers regarding RC diagnosis, monitoring, and prognosis is crucial. Objectives: The purpose of the present study was to evaluate the differential expression of the Aquaporin (AQP) gene family network in RC, and the effect of Radiotherapy (RT) on their expression profile, to indicate novel biomarkers and prognostic factors. Methods: We used data mining techniques to construct the network of the AQP-associated genes to determine the Differentially Expressed Genes (DEGs) in RC and in irradiated as compared to nonirradiated RC patients. Furthermore, survival data of The Cancer Genome Atlas (TCGA) were analysed to assess the prognostic role of the DEGs, along with the functional enrichment of gene ontologies and miRNAs related to the DEGs in RC. Results: Microarray data of one PubMed GEO dataset was extracted, incorporating 22 RC and 20 normal rectal tissue samples. Eight DEGs were reported. Four DEGs were up-regulated and four downregulated in RC. Correlations were identified among the DEGs. Deming regression analysis was performed in order to demonstrate the equations describing these correlations. One gene (Aquaporin 3) was downregulated in irradiated RC samples compared with non-irradiated samples. The most significantly affected biological pathways and miRNAs were identified by functional enrichment analysis. Conclusion: The present study demonstrates an eight-gene molecular panel that could facilitate as biomarkers regarding RC patients, which are potential targets of five miRNA families. Finally, our results highlight the effect of radiotherapy on AQPs and the associated pathways in RC.

scholarly journals CCTs as new biomarkers for the prognosis of head and neck squamous cancer

Open Medicine ◽  
2020 ◽  
Vol 15 (1) ◽  
pp. 672-688
Author(s):  
Yanbo Dong ◽  
Siyu Lu ◽  
Zhenxiao Wang ◽  
Liangfa Liu
Keyword(s):  
Head And Neck ◽  
Survival Data ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Advanced Tumor Stage ◽  
Expression Levels ◽  
T Complex ◽  
Advanced Tumor ◽  
Squamous Cancer

AbstractThe chaperonin-containing T-complex protein 1 (CCT) subunits participate in diverse diseases. However, little is known about their expression and prognostic values in human head and neck squamous cancer (HNSC). This article aims to evaluate the effects of CCT subunits regarding their prognostic values for HNSC. We mined the transcriptional and survival data of CCTs in HNSC patients from online databases. A protein–protein interaction network was constructed and a functional enrichment analysis of target genes was performed. We observed that the mRNA expression levels of CCT1/2/3/4/5/6/7/8 were higher in HNSC tissues than in normal tissues. Survival analysis revealed that the high mRNA transcriptional levels of CCT3/4/5/6/7/8 were associated with a low overall survival. The expression levels of CCT4/7 were correlated with advanced tumor stage. And the overexpression of CCT4 was associated with higher N stage of patients. Validation of CCTs’ differential expression and prognostic values was achieved by the Human Protein Atlas and GEO datasets. Mechanistic exploration of CCT subunits by the functional enrichment analysis suggests that these genes may influence the HNSC prognosis by regulating PI3K-Akt and other pathways. This study implies that CCT3/4/6/7/8 are promising biomarkers for the prognosis of HNSC.

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 Identifying and characterizing lincRNA genomic clusters reveals its cooperative functions in human cancer

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Hanxiao Zhou ◽  
Yue Gao ◽  
Xin Li ◽  
Shipeng Shang ◽  
Peng Wang ◽  
...  
Keyword(s):  
Human Cancer ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Computational Method ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Pathophysiological Mechanism ◽  
Multiple Cancer ◽  
Cancer Types ◽  
Genomic Clusters

Abstract Background Emerging evidence has revealed that some long intergenic non-coding RNAs (lincRNAs) are likely to form clusters on the same chromosome, and lincRNA genomic clusters might play critical roles in the pathophysiological mechanism. However, the comprehensive investigation of lincRNA clustering is rarely studied, particularly the characterization of their functional significance across different cancer types. Methods In this study, we firstly constructed a computational method basing a sliding window approach for systematically identifying lincRNA genomic clusters. We then dissected these lincRNA genomic clusters to identify common characteristics in cooperative expression, conservation among divergent species, targeted miRNAs, and CNV frequency. Next, we performed comprehensive analyses in differentially-expressed patterns and overall survival outcomes for patients from The Cancer Genome Atlas (TCGA) and The Genotype-Tissue Expression (GTEx) across multiple cancer types. Finally, we explored the underlying mechanisms of lincRNA genomic clusters by functional enrichment analysis, pathway analysis, and drug-target interaction. Results We identified lincRNA genomic clusters according to the algorithm. Clustering lincRNAs tended to be co-expressed, highly conserved, targeted by more miRNAs, and with similar deletion and duplication frequency, suggesting that lincRNA genomic clusters may exert their effects by acting in combination. We further systematically explored conserved and cancer-specific lincRNA genomic clusters, indicating they were involved in some important mechanisms of disease occurrence through diverse approaches. Furthermore, lincRNA genomic clusters can serve as biomarkers with potential clinical significance and involve in specific pathological processes in the development of cancer. Moreover, a lincRNA genomic cluster named Cluster127 in DLK1-DIO3 imprinted locus was discovered, which contained MEG3, MEG8, MEG9, MIR381HG, LINC02285, AL132709.5, and AL132709.1. Further analysis indicated that Cluster127 may have the potential for predicting prognosis in cancer and could play their roles by participating in the regulation of PI3K-AKT signaling pathway. Conclusions Clarification of the lincRNA genomic clusters specific roles in human cancers could be beneficial for understanding the molecular pathogenesis of different cancer types.

scholarly journals Long Noncoding RNA RP11-357H14.17 Plays an Oncogene Role in Gastric Cancer by Activating ATF2 Signaling and Enhancing Treg Cells

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Tang Xiaoli ◽  
Wang Wenting ◽  
Zhang Meixiang ◽  
Zuo Chunlei ◽  
Hu Chengxia
Keyword(s):  
Gastric Cancer ◽  
Noncoding Rna ◽  
Prognostic Value ◽  
Malignant Tumors ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Treg Cells ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Biological Behavior

Background. Gastric cancer (GC) is one of the most common malignant tumors in the world. The potential functions and mechanisms of long noncoding RNAs (lncRNAs) in GC development are still unclear. It is of great significance to explore the prognostic value of LncRNA signatures for GC. Methods. LncRNAs differently expressed in GC and their prognostic value were studied based on The Cancer Genome Atlas (TCGA) database. The functional regulatory network and immune infiltration of RP11-357H14.17 were further studied using a variety of bioinformatics tools and databases. Results. We found that the high expression of RP11-357H14.17 was closely associated with shortened overall survival (OS) and poor prognosis in gastric cancer patients. We also found that its expression was related to clinical features including tumor volume, metastasis, and differentiation. Functional enrichment analysis revealed that RP11-357H14.17 is closely related to enhanced DNA replication and metabolism; ssGSEA analysis implied the oncogenic roles of RP11-357H14.17 was related to ATF2 signaling and Treg cell differentiation. Furthermore, we verified such link by using real-time PCR and IHC staining in human GC samples. Conclusion. We demonstrate that RP11-357H14.17 may play a crucial role in the occurrence, development, and malignant biological behavior of gastric cancer as a potential prognostic marker for gastric cancer.

scholarly journals Identification of long noncoding RNA RP11-89K21.1 and RP11-357H14.17 as prognostic signature of endometrial carcinoma via integrated bioinformatics analysis

2020 ◽  
Author(s):  
Lingling Gao ◽  
Xin Nie ◽  
Wenchao Zhang ◽  
Rui Gou ◽  
Yuexin Hu ◽  
...  
Keyword(s):  
Endometrial Carcinoma ◽  
Noncoding Rna ◽  
Growth Regulation ◽  
Malignant Tumors ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Biological Behavior ◽  
Vessel Development

Abstract Background: Endometrial carcinoma (EC) is one of the most common malignant tumors in gynecology. The potential functions and mechanisms of long noncoding RNAs (lncRNAs) in the occurrence and progression of EC remains unclear. It’s meaningful to explore lncRNAs signature for providing prognostic value of EC. Methods：The differentially expressed lncRNAs and their prognostic values in EC were investigated based on The Cancer Genome Atlas (TCGA) database; the transcriptional factors (TFs), the competing endogenous RNA (ceRNA) mechanism, functional regulatory network and immune infiltration of RP11-89K21.1 and RP11-357H14.17 were further explored by various bioinformatics tools and databases. Results: We first identified high expression of RP11-89K21.1 and RP11-357H14.17 were closely associated with shorten overall survival (OS) and poor prognosis in patients with EC. We also elucidated the networks of transcription factor and co-expression genes associated with RP11-89K21.1 and RP11-357H14.17. Furthermore, the ceRNA network mechanism was successfully constructed through 2 lncRNAs (RP11-89K21.1 and RP11-357H14.17), 11 miRNAs and 183 mRNAs. Functional enrichment analysis revealed that the targeting genes of RP11-89K21.1 and RP11-357H14.17 were strongly associated with microRNAs in cancer, vessel development, growth regulation, growth factor and cell differentiation, and involved in pathways including pathways in cancer, microRNAs in cancer and apoptotic signaling pathway. Conclusions: We demonstrated for the first time that RP11-89K21.1 and RP11-357H14.17 may play crucial roles in the occurrence, development and malignant biological behavior of EC, and can be regarded as potential prognostic biomarkers for EC.

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

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.

scholarly journals Identification of Key Genes Affecting the Tumor Microenvironment and Prognosis of Triple-Negative Breast Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Jiarong Yi ◽  
Wenjing Zhong ◽  
Haoming Wu ◽  
Jikun Feng ◽  
Xiazi Zouxu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Tumor Microenvironment ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Key Genes

Although the tumor microenvironment (TME) plays an important role in the development of many cancers, its roles in breast cancer, especially triple-negative breast cancer (TNBC), are not well studied. This study aimed to identify genes related to the TME and prognosis of TNBC. Firstly, we identified differentially expressed genes (DEG) in the TME of TNBC, using Expression data (ESTIMATE) datasets obtained from the Cancer Genome Atlas (TCGA) and Estimation of Stromal and Immune cells in Malignant Tumor tissues. Next, survival analysis was performed to analyze the relationship between TME and prognosis of TNBC, as well as determine DEGs. Genes showing significant differences were scored as alternative genes. A protein-protein interaction (PPI) network was constructed and functional enrichment analysis conducted using the DEG. Proteins with a degree greater than 5 and 10 in the PPI network correspond with hub genes and key genes, respectively. Finally, CCR2 and CCR5 were identified as key genes in TME and prognosis of TNBC. Finally, these results were verified using Gene Expression Omnibus (GEO) datasets and immunohistochemistry of TNBC patients. In conclusion, CCR2 and CCR5 are key genes in the TME and prognosis of TNBC with the potential of prognostic biomarkers in TNBC.

scholarly journals Development and Validation of a Prognostic Classifier Based on Metabolism-Related Genes for Breast Cancer

2021 ◽  
Author(s):  
Nan Wang ◽  
Lin Li ◽  
Jiangrui Chi ◽  
Xinwei Liu ◽  
Youyi Xiong ◽  
...  
Keyword(s):  
Lipid Metabolism ◽  
Cox Regression ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Marker Genes ◽  
Tissue Samples ◽  
Prognostic Gene

Abstract Background: Alterations in lipid metabolism have been implicated in the development of many tumors. However, the contribution of different lipid metabolism pathways to Breast invasive carcinoma (BRCA) remains to be fully established. Here, we attempted to ascertain the prognostic value of lipid metabolism-related genes in BRCA. Methods: We obtained RNA expression data and clinical information for BRCA and normal samples from public databases and downloaded a lipid metabolism-related gene set to harvest lipid metabolism-related genes. IPA was applied to identify the potential pathways and functions of DEGs related to lipid metabolism. Subsequently, univariate and multivariate Cox regression analyses were utilized to construct the prognostic gene signature and independent prognostic analyses. Thereafter, the differential expression of the selected marker genes SDC1 and SORBS1 in clinical tissue samples was verified by qRT-PCR, western blotting, and immunohistochemical experiments. Functional enrichment analysis of prognostic genes was achieved by the GO and KEGG databases. Moreover, Kaplan-Meier analysis, ROC curves, clinical immunohistochemistry conditions and follow-up results were employed to assess the prognostic potency. Potential compounds targeting prognostic genes were then screened by CMap database and a prognostic gene-drug interaction network was constructed using Comparative Toxicogenomics Database.Results: IPA demonstrated that the 162 lipid metabolism-related DEGs we obtained were involved in a variety of lipid metabolism and BRCA pathological signatures. Subsequent functional enrichment analysis of candidate prognostic lipid metabolism DEGs also revealed a similar outcome. The prognostic classifier we constructed comprising SDC1 and SORBS1 has a strong prognostic potency that was verified by the clinical conditions and follow-up results, it also can serve as an independent prognostic marker for BRCA. CMap filtered 37 potential compounds against prognostic genes. CTD indicated that the two prognostic genes had 16 drugs in common. Conclusion: Within this study, we identified a novel prognostic classifier based on two lipid metabolism-related genes: SDC1 and SORBS1. This classifier had accurately predicted the prognosis of our follow-up BRCA patients and this result highlighted a new perspective on the metabolic exploration of BRCA. In addition, SDC1 and SORBS1 could serve as a possible new target for the synthesis of BRCA drugs.

scholarly journals Predictive Proteomic Signature for the Prognosis of Cervical Cancer

2021 ◽  
Author(s):  
Xiaoyu Ji ◽  
Guangdi Chu ◽  
Jinwen Jiao ◽  
Teng Lv ◽  
Yulong Chen ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Regression Analysis ◽  
Cox Regression ◽  
Predictive Ability ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Cox Regression Analysis ◽  
Incidence And Mortality

Abstract Objective: Cervical cancer (CC) is one of the most common types of malignant female cancer, and its incidence and mortality are not optimistic. Protein panels can be a powerful prognostic factor for many types of cancer. The purpose of our study was to investigate a proteomic panel to predict survival of patients with common CC. Methods and results: The protein expression and clinicopathological data of CC were downloaded from The Cancer Proteome Atlas (TCPA) and The Cancer Genome Atlas (TCGA) database, respectively. We selected the prognosis-related proteins (PRPs) by univariate Cox regression analysis and found that the results of functional enrichment analysis were mainly related to apoptosis. We used Kaplan–Meier(K-M) analysis and multivariable Cox regression analysis further to screen PRPs to establish a prognostic model, including BCL2, SMAD3, and 4EBP1-pT70. The signature was verified to be independent predictors of OS by Cox regression analysis and the Area Under Curves. Nomogram and subgroup classification were established based on the signature to verify its clinical application. Furthermore, we looked for the co-expressed proteins of three-protein panel as potential prognostic proteins.Conclusion: A proteomic signature independently predicted OS of CC patients, and the predictive ability was better than the clinicopathological characteristics. This signature can help improve prediction for clinical outcome and provides new targets for CC treatment.

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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