Transcriptomic and microRNA Expression Profiles Identify Biomarkers for Predicting Neo-Chemoradiotherapy Response in Esophageal Squamous Cell Carcinomas (ESCC)

Neo-chemoradiotherapy (nCRT) before surgery is a standard treatment for locally advanced esophageal cancers. However, the treatment outcome of nCRT varied with different patients. This study aimed to identify potential biomarkers for prediction of nCRT-response in patients with esophageal squamous cell carcinoma (ESCC). Microarray datasets of nCRT responder and non-responder samples (access number GSE45670 and GSE59974) of patients with ESCC were downloaded from Gene Expression Omnibus (GEO) database. The mRNA expression profiles of cancer biopsies from four ESCC patients were analyzed before and after nCRT. Differentially expressed genes (DEGs) and miRNAs were screened between nCRT responder and non-responder ESCC samples. Functional enrichment analysis was conducted for these DEGs followed by construction of protein-protein interaction (PPI) network and miRNA-mRNA regulatory network. Finally, univariate survival analysis was performed to identify candidate biomarkers with prognostic values in ESCC. We identified numerous DEGs and differentially expressed miRNAs from nCRT responder group. GO and KEGG analysis showed that the dysregulated genes were mainly involved in biological processes and pathways, including “response to stimulus”, “cellular response to organic substance”, “regulation of signal transduction”, “AGE-RAGE signaling pathway in diabetic complications”, and “steroid hormone biosynthesis”. After integration of PPI network and miRNA-mRNA network analysis, we found eight genes, TNF, AKR1C1, AKR1C2, ICAM1, GPR68, GNB4, SERPINE1 and MMP12, could be candidate genes associated with disease progression. Univariate cox regression analysis showed that there was no significant correlation between dysregulated miRNAs (such as hsa-miR-34b-3p, hsa-miR-127-5p, hsa-miR-144-3p, and hsa-miR-486-5p, et al.) and overall survival of ESCC patients. Moreover, abnormal expression of MMP12 was significantly correlated with pathological degree, TNM stage, lymph nodes metastasis, and overall survival of ESCC patients (p < 0.05). Taken together, our study identified that MMP12 might be a useful tumor biomarker and therapeutic target for ESCC.

Download Full-text

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.

Download Full-text

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

10.21203/rs.3.rs-618453/v1 ◽

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.

Download Full-text

Development of an immunogenomics-based prognostic index model of laryngeal squamous cell carcinoma

10.21203/rs.3.rs-56339/v1 ◽

2020 ◽

Author(s):

Yujie Shen ◽

Han Zhou ◽

Shikun Dong ◽

Meiping Lu ◽

Weida Dong ◽

...

Keyword(s):

Squamous Cell Carcinoma ◽

Cell Carcinoma ◽

Squamous Cell ◽

Laryngeal Squamous Cell Carcinoma ◽

Cox Regression ◽

Prognostic Index ◽

Functional Enrichment ◽

Differentially Expressed ◽

Receptor Interaction ◽

Cox Regression Analysis

Abstract Background: The immune system greatly affects the prognosis of various malignancies. Studies on differentially expressed immune-related genes (IRGs) in the immune microenvironment of laryngeal squamous cell carcinoma (LSCC) have rarely been reported.Methods: In this paper, the prognostic potentials of IRGs in LSCC were explored. The RNAseq dataset containing differentially expressed IRGs and corresponding clinical information of LSCC patients was obtained from The Cancer Genome Atlas (TCGA). A total of 371 up-regulated and 61 down-regulated IRGs were identified. Subsequent functional enrichment analysis revealed that the pathway of IRGs was mainly enriched in the cytokine-cytokine receptor interaction. Then, 30 IRGs with prognostic potentials in LSCC were screened out, and the regulatory network induced by relevant transcription factors (TFs) were constructed.Results: Finally, multivariate Cox regression analysis was conducted to assess the prognostic potential of 15 IRGs after adjustment of clinical factors and LSCC patients were classified into 2 subgroups based on different outcomes. The gene expression of the model was verified by other independent databases. Nomogram including the 15 IRGs signature was established and shown some clinical net beneft. Intriguingly, B cells were significantly enriched in the low-risk group. Conclusion:These findings may contribute to the development of potential therapeutic targets and biomarkers for the new-immunotherapy of LSCC.

Download Full-text

Identification of an autophagy-related gene expression prognostic model in endometrial carcinoma patients

10.21203/rs.3.rs-32147/v1 ◽

2020 ◽

Author(s):

Pinping Jiang ◽

Wei Sun ◽

Ningmei Shen ◽

Qiang Wang ◽

Shouyu Wang ◽

...

Keyword(s):

Clinical Features ◽

Prognostic Model ◽

Cox Regression ◽

Expression Profiles ◽

Enrichment Analysis ◽

Functional Enrichment ◽

Differentially Expressed ◽

Pathway Enrichment Analysis ◽

Prognostic Signature ◽

Cox Regression Analysis

Abstract Background Autophagy, as a lysosomal degradation pathway, has been reported to be involved in various pathologies, including cancer. However, the expression profiles of autophagy-related genes (ARGs) in endometrial cancer (EC) remain poorly understood. Methods In this study, we analyzed the expression of MRGs using The Cancer Genome Atlas (TCGA) data to screen differentially expressed MRGs (DE-MRGs) significantly correlated to EC patients’ prognosis. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DE-MRGs were investigated. LASSO algorithm and Cox regression analysis were performed to select MRGs closely related to EC patients’ outcomes. A prognostic signature was developed and the efficacy were validated in part of and the entire TCGA EC cohort. Moreover, we developed a comprehensive nomogram including the risk model and clinical features to predict EC patients' survival probability. Results Ninety-four ARGs significantly dysregulated in EC samples compared with the normal control samples. Functional enrichment analysis showed these differentially expressed ARGs (DE-ARGs) were highly enriched in apoptosis, P53 signaling pathway, and various cancer development. Among the 94 DE-ARGs, we subsequently screen out four-ARGs closely related to EC patients outcomes, which are ERBB2, PTEN, TP73 and ARSA. Based on the expression and coefficiency of 4 DE-ARGs, we developed a prognostic signature and further validated its efficacy in part of and the entire TCGA EC cohort. The four ARGs signature was independent of other clinical features, and was proved to effectively distinguish high- or low-risk EC patients and predicted patients' OS accurately. Moreover, the nomogram showed the excellent consistency between the prediction and actual observation in terms of patients' 3- and 5-year survival rates. Conclusions It was suggested that the ARG prognostic model and the comprehensive nomogram may guide the precise outcome prediction and rational therapy in clinical practice.

Download Full-text

Competitive endogenous RNA (ceRNA) regulation network of lncRNAs, miRNAs, and mRNAs in Wilms tumour

10.21203/rs.2.11185/v6 ◽

2019 ◽

Author(s):

fucai tang ◽

zechao Lu ◽

jiamin wang ◽

zhibiao Li ◽

weijia Wu ◽

...

Keyword(s):

Overall Survival ◽

Transcription Factor ◽

Rna Polymerase Ii ◽

Regulatory Network ◽

Expression Profiles ◽

Functional Enrichment ◽

Differentially Expressed ◽

Proximal Promoter ◽

Wilms Tumour ◽

Cerna Network

Abstract Background: Competitive endogenous RNAs (ceRNAs) have revealed a new mechanism of interaction between RNAs. However, an understanding of the ceRNA regulatory network in Wilms tumour (WT) remains limited.Methods: The expression profiles of mRNAs, miRNAs and lncRNAs in Wilms tumour samples and normal samples were obtained from the Therapeutically Applicable Research to Generate Effective Treatment (TARGET) database. The EdgeR package was employed to identify differentially expressed lncRNAs, miRNAs and mRNAs. Functional enrichment analyses via the ClusterProfile R package were performed, and the lncRNA–miRNA–mRNA interaction ceRNA network was established in Cytoscape. Subsequently, the correlation between the ceRNA network and overall survival was analysed.Results: A total of 2,037 lncRNAs, 154 miRNAs and 3,609 mRNAs were identified as differentially expressed RNAs in Wilms tumour. Of those, 205 lncRNAs, 26 miRNAs and 143 mRNAs were included in the ceRNA regulatory network. The results of Gene Ontology (GO) analysis revealed that the differentially expressed genes (DEGs) were mainly enriched in terms related to response to mechanical stimuli, transcription factor complexes, and transcription factor activity (related to RNA polymerase II proximal promoter sequence-specific DNA binding). The results of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that the DEGs were mainly enriched in pathways related to the cell cycle. The survival analysis results showed that 16 out of the 205 lncRNAs, 1 out of 26 miRNAs and 5 out of 143 mRNAs were associated with overall survival in Wilms tumour patients (P < 0.05).Conclusions: CeRNA networks play an important role in Wilms tumour. This finding might provide effective, novel insights for further understanding the mechanisms underlying Wilms tumour.

Download Full-text

Competitive Endogenous RNA (ceRNA) Regulation Network of lncRNA-miRNA-mRNA in Wilms tumor

10.21203/rs.2.11185/v3 ◽

2019 ◽

Author(s):

fucai tang ◽

zechao Lu ◽

jiamin wang ◽

zhibiao Li ◽

weijia Wu ◽

...

Keyword(s):

Overall Survival ◽

Transcription Factor ◽

Regulatory Network ◽

Wilms Tumor ◽

Expression Profiles ◽

Functional Enrichment ◽

Differentially Expressed ◽

Proximal Promoter ◽

Cerna Network ◽

Competitive Endogenous Rna

Abstract Background: Competitive endogenous RNA (ceRNA) have revealed a new mechanism of interaction between RNAs. However, such comprehension of the ceRNA regulatory network in Wilms tumor remains limited.Methods: The expression profiles regarding mRNAs, miRNAs and lncRNAs on Wilms tumor samples and normal samples were obtained from Therapeutically Applicable Research to Generate Effective Treatment (TARGET) database. EdgeR package was applied to identify differentially expressed lncRNAs, miRNAs and mRNAs. Functional enrichment analyses via the ClusterProfile R package was done. following which the lncRNA–miRNA–mRNA interaction ceRNA network was established in Cytoscape . Subsequently, correlation between ceRNA network and overall survival prognosis were analyzed. Results: A total of 2,037 lncRNAs, 154 miRNAs and 3,609 mRNAs were identified as differentially expressed RNAs in Wilms tumor. 205 lncRNAs, 26 miRNAs and 143 mRNAs were included in ceRNA regulatory network. The result of Gene Ontology analysis revealed that DEGs were enriched mainly in response to mechanical stimulus, transcription factor complex, and transcription factor activity (RNA polymerase II proximal promoter sequence-specific DNA binding). The result of the Kyoto Encyclopedia of Genes and Genomes pathway analysis showed that DEGs were enriched mainly in cell cycle. Survival analysis results showed that 14 out of the 205 lncRNAs, 1 out of 26 miRNAs and 8 out of 143 mRNAs were associated with overall survival in Wilms tumor patients (P < 0.05). Conclusions: CeRNA networks played an important role in Wilms tumor. This might provide effective novel insights for further understanding of the mechanisms underlying Wilms tumor.

Download Full-text

Competitive endogenous RNA (ceRNA) regulation network of lncRNAs, miRNAs, and mRNAs in Wilms tumour

10.21203/rs.2.11185/v5 ◽

2019 ◽

Author(s):

fucai tang ◽

zechao Lu ◽

jiamin wang ◽

zhibiao Li ◽

weijia Wu ◽

...

Keyword(s):

Overall Survival ◽

Transcription Factor ◽

Rna Polymerase Ii ◽

Regulatory Network ◽

Expression Profiles ◽

Functional Enrichment ◽

Differentially Expressed ◽

Proximal Promoter ◽

Wilms Tumour ◽

Cerna Network

Download Full-text

Identification of a metabolism-related gene expression prognostic model in endometrial carcinoma patients

10.21203/rs.3.rs-35652/v1 ◽

2020 ◽

Author(s):

Pinping Jiang ◽

Wei Sun ◽

Ningmei Shen ◽

Xiaohao Huang ◽

Shilong Fu

Keyword(s):

Clinical Features ◽

Prognostic Model ◽

Cox Regression ◽

Expression Profiles ◽

Enrichment Analysis ◽

Functional Enrichment ◽

Differentially Expressed ◽

Pathway Enrichment Analysis ◽

Prognostic Signature ◽

Cox Regression Analysis

Abstract Background: Metabolic abnormalities have recently been widely studied in various cancer types. This study aims to explore the expression profiles of metabolism-related genes (MRGs) in endometrial cancer (EC). Methods: We analyzed the expression of MRGs using The Cancer Genome Atlas (TCGA) data to screen differentially expressed MRGs (DE-MRGs) significantly correlated to EC patients’ prognosis. Functional pathway enrichment analysis of DE-MRGs were investigated. LASSO algorithm and Cox regression analysis were performed to select MRGs closely related to EC patients’ outcomes. A prognostic signature was developed and the efficacy were validated in part of and the entire TCGA EC cohort. Moreover, we developed a comprehensive nomogram including the risk model and clinical features to predict EC patients' survival probability.Results: Forty-seven differentially expressed MRGs (DE-MRGs) were significantly correlate to EC patients’ prognosis. Functional enrichment analysis showed these MRGs were highly enriched in amino acid, glycolysis, and glycerophospholipid metabolism. Nine MRGs were screened out to closely relate to EC patients’ outcomes, which are CYP4F3, CEL, GPAT3, LYPLA2, HNMT, PHGDH, CKM, UCK2 and ACACB. Based on nine DE-MRGs, we developed a prognostic signature and its efficacy in part of and the entire TCGA EC cohort was validated. The nine-MRGs signature was independent of other clinical features, and could effectively distinguish high- or low-risk EC patients and predicted patients' OS. The nomogram showed excellent consistency between prediction and actual survival observation. Conclusions: The MRG prognostic model and the comprehensive nomogram could guide for precise outcom predicting and rational therapy in clinical practice.

Download Full-text

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

Journal of Healthcare Engineering ◽

10.1155/2021/3761858 ◽

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.

Download Full-text

Establishment and validation of an RNA binding protein-associated prognostic model for ovarian cancer

Journal of Ovarian Research ◽

10.1186/s13048-021-00777-1 ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Chaofan He ◽

Fuxin Huang ◽

Kejia Zhang ◽

Jun Wei ◽

Ke Hu ◽

...

Keyword(s):

Ovarian Cancer ◽

Target Genes ◽

Rna Binding ◽

Cox Regression ◽

Binding Protein ◽

Expression Profiles ◽

Rna Binding Protein ◽

Functional Enrichment ◽

Differentially Expressed ◽

Cox Regression Analysis

Abstract Background Ovarian cancer (OC) is one of the most common gynecological malignant tumors worldwide, with high mortality and a poor prognosis. As the early symptoms of malignant ovarian tumors are not obvious, the cause of the disease is still unclear, and the patients’ postoperative quality of life of decreases. Therefore, early diagnosis is a problem requiring an urgent solution. Methods We obtained the gene expression profiles of ovarian cancer and normal samples from TCGA and GTEx databases for differential expression analysis. From existing literature reports, we acquired the RNA-binding protein (RBP) list for the human species. Utilizing the online tool Starbase, we analyzed the interaction relationship between RBPs and their target genes and selected the modules of RBP target genes through Cytoscape. Finally, univariate and multivariate Cox regression analyses were used to determine the prognostic RBP signature. Results We obtained 527 differentially expressed RBPs, which were involved in many important cellular events, such as RNA splicing, the cell cycle, and so on. We predicted several target genes of RBPs, constructed the interaction network of RBPs and their target genes, and obtained many modules from the Cytoscape analysis. Functional enrichment of RBP target genes also includes these important biological processes. Through Cox regression analysis, OC prognostic RBPs were identified and a 10-RBP model constructed. Further analysis showed that the model has high accuracy and sensitivity in predicting the 3/5-year survival rate. Conclusions Our study identified differentially expressed RBPs and their target genes in OC, and the results promote our understanding of the molecular mechanism of ovarian cancer. The current study could develop novel biomarkers for the diagnosis, treatment, and prognosis of OC and provide new ideas and prospects for future clinical research.

Download Full-text