Four Novel Prognostic Genes Related to Prostate Cancer Identified Using Co-expression Structure Network Analysis

Prostate cancer (PCa) is one of the most common malignancies for males, but very little is known about its pathogenesis. This study aimed to identify novel biomarkers associated with PCa prognosis and elucidate the underlying molecular mechanism. First, The Cancer Genome Atlas (TCGA) RNA-sequencing data were utilized to identify differentially expressed genes (DEGs) between tumor and normal samples. The DEGs were then applied to construct a co-expression and mined using structure network analysis. The magenta module that was highly related to the Gleason score (r = 0.46, p = 3e–26) and tumor stage (r = 0.38, p = 2e–17) was screened. Subsequently, all genes of the magenta module underwent function annotation. From the key module, CCNA2, CKAP2L, NCAPG, and NUSAP1 were chosen as the four candidate genes. Finally, internal (TCGA) and external data sets (GSE32571, GSE70770, and GSE141551) were combined to validate and predict the value of real hub genes. The results show that the above genes are up-regulated in PCa samples, and higher expression levels show significant association with higher Gleason scores and tumor T stage. Moreover, receiver operating characteristic curve and survival analysis validate the excellent value of hub genes in PCa progression and prognosis. In addition, the protein levels of these four genes also remain higher in tumor tissues when compared with normal tissues. Gene set enrichment analysis and gene set variation analysis for a single gene reveal the close relation with cell proliferation. Meanwhile, 11 small molecular drugs that have the potential to treat PCa were also screened. In conclusion, our research identified four potential prognostic genes and several candidate molecular drugs for treating PCa.

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Development and validation of 10-gene signature for predicting survival in patients with prostate cancer

10.21203/rs.3.rs-17658/v1 ◽

2020 ◽

Author(s):

Nan Li ◽

Kai Yu ◽

Ling Zhong ◽

Dingyuan Zeng

Keyword(s):

Prostate Cancer ◽

Cancer Patients ◽

High Throughput Sequencing ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Cancer Prognosis ◽

Sequencing Data

Abstract Background. The prognosis for prostate cancer patients remains poor. High-throughput sequencing data provide a solid basis for identifying genes associated with cancer prognosis, but genetic markers are needed to predict the clinical outcome of prostate cancer. Methods. The Cancer Genome Atlas (TCGA) database (N = 551) was adopted to estimate the prognostic value of immune genes. RNA-seq and clinical follow-up data were downloaded from TCGA. The samples were randomly divided into training and test. Cox regression analyses and least absolute shrinkage and selection operator (LASSO) were conducted to develop an immune risk score. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and single sample Gene Set Enrichment Analysis (ssGSEA) were used for functional Analysis. Tumor Immune Estimation Resource (TIMER) is used to analyze the immune score, and RMS curve and clinical decision curve analysis is used to analyze the superiority of the comparison with published models. Results. Survival analyses revealed that 19 genes significantly associated with the overall survival (OS). 10-genes signature was ultimately obtained through random forest feature selection. Riskscore effectively stratified samples in the training, test, and external verification sets and all TCGA sets. The 5-year survival AUC in the training, verification sets and all TCGA sets were around 0.7. Univariate and multivariate analysis showed that 10-genes signature has good predictive performance in clinical. TIMER analysis shows that immunosuppression may reduce the chances of survival for patients with prostate cancer. Compared with published models, our model has a higher C-index. Conclusion. We constructed a 10-gene signature as a new prognostic marker for predicting survival of prostate cancer patients.

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HEDGEHOG/GLI Modulates the PRR11-SKA2 Bidirectional Transcription Unit in Lung Squamous Cell Carcinomas

Genes ◽

10.3390/genes12010120 ◽

2021 ◽

Vol 12 (1) ◽

pp. 120

Author(s):

Yiyun Sun ◽

Dandan Xu ◽

Chundong Zhang ◽

Yitao Wang ◽

Lian Zhang ◽

...

Keyword(s):

Rna Sequencing ◽

Squamous Cell ◽

Gene Pair ◽

Gene List ◽

Ectopic Expression ◽

Lung Squamous Cell Carcinoma ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Sequencing Data ◽

Gene Set

We previously demonstrated that proline-rich protein 11 (PRR11) and spindle and kinetochore associated 2 (SKA2) constituted a head-to-head gene pair driven by a prototypical bidirectional promoter. This gene pair synergistically promoted the development of non-small cell lung cancer. However, the signaling pathways leading to the ectopic expression of this gene pair remains obscure. In the present study, we first analyzed the lung squamous cell carcinoma (LSCC) relevant RNA sequencing data from The Cancer Genome Atlas (TCGA) database using the correlation analysis of gene expression and gene set enrichment analysis (GSEA), which revealed that the PRR11-SKA2 correlated gene list highly resembled the Hedgehog (Hh) pathway activation-related gene set. Subsequently, GLI1/2 inhibitor GANT-61 or GLI1/2-siRNA inhibited the Hh pathway of LSCC cells, concomitantly decreasing the expression levels of PRR11 and SKA2. Furthermore, the mRNA expression profile of LSCC cells treated with GANT-61 was detected using RNA sequencing, displaying 397 differentially expressed genes (203 upregulated genes and 194 downregulated genes). Out of them, one gene set, including BIRC5, NCAPG, CCNB2, and BUB1, was involved in cell division and interacted with both PRR11 and SKA2. These genes were verified as the downregulated genes via RT-PCR and their high expression significantly correlated with the shorter overall survival of LSCC patients. Taken together, our results indicate that GLI1/2 mediates the expression of the PRR11-SKA2-centric gene set that serves as an unfavorable prognostic indicator for LSCC patients, potentializing new combinatorial diagnostic and therapeutic strategies in LSCC.

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Key pathways involved in prostate cancer based on gene set enrichment analysis and meta analysis

Genetics and Molecular Research ◽

10.4238/2011.december.14.10 ◽

2011 ◽

Vol 10 (4) ◽

pp. 3856-3887 ◽

Cited By ~ 11

Author(s):

Q.Y. Ning ◽

J.Z. Wu ◽

N. Zang ◽

J. Liang ◽

Y.L. Hu ◽

...

Keyword(s):

Prostate Cancer ◽

Meta Analysis ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Gene Set Enrichment ◽

Gene Set ◽

Key Pathways

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Development of a Novel Prognostic Signature Based on Antigen Processing and Presentation in Patients with Breast Cancer

Pathology & Oncology Research ◽

10.3389/pore.2021.600727 ◽

2021 ◽

Vol 27 ◽

Author(s):

Aoshuang Qi ◽

Mingyi Ju ◽

Yinfeng Liu ◽

Jia Bi ◽

Qian Wei ◽

...

Keyword(s):

Breast Cancer ◽

Antigen Processing ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Prognostic Indicators ◽

Gene Set ◽

Antigen Processing And Presentation

Background: Complex antigen processing and presentation processes are involved in the development and progression of breast cancer (BC). A single biomarker is unlikely to adequately reflect the complex interplay between immune cells and cancer; however, there have been few attempts to find a robust antigen processing and presentation-related signature to predict the survival outcome of BC patients with respect to tumor immunology. Therefore, we aimed to develop an accurate gene signature based on immune-related genes for prognosis prediction of BC.Methods: Information on BC patients was obtained from The Cancer Genome Atlas. Gene set enrichment analysis was used to confirm the gene set related to antigen processing and presentation that contributed to BC. Cox proportional regression, multivariate Cox regression, and stratified analysis were used to identify the prognostic power of the gene signature. Differentially expressed mRNAs between high- and low-risk groups were determined by KEGG analysis.Results: A three-gene signature comprising HSPA5 (heat shock protein family A member 5), PSME2 (proteasome activator subunit 2), and HLA-F (major histocompatibility complex, class I, F) was significantly associated with OS. HSPA5 and PSME2 were protective (hazard ratio (HR) < 1), and HLA-F was risky (HR > 1). Risk score, estrogen receptor (ER), progesterone receptor (PR) and PD-L1 were independent prognostic indicators. KIT and ACACB may have important roles in the mechanism by which the gene signature regulates prognosis of BC.Conclusion: The proposed three-gene signature is a promising biomarker for estimating survival outcomes in BC patients.

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Mining novel cell glycolysis related gene markers that can predict the survival of colon adenocarcinoma patients

Bioscience Reports ◽

10.1042/bsr20201427 ◽

2020 ◽

Vol 40 (8) ◽

Author(s):

Sihan Chen ◽

Guodong Cao ◽

Wei Wu ◽

Yida Lu ◽

Xiaobo He ◽

...

Keyword(s):

Cox Regression ◽

Mechanistic Model ◽

Single Gene ◽

Colon Adenocarcinoma ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Risk Scores ◽

Gene Markers ◽

Cox Regression Analysis

Abstract Colon adenocarcinoma (COAD) is a malignant gastrointestinal tumor, often occurring in the left colon, which is regulated by glycolysis-related processes. In past studies, multiple genes that influence the prognosis for survival have been discovered through bioinformatics analysis. However, the prediction of disease prognosis using a single gene is not an accurate method. In the present study, a mechanistic model was established to achieve better prediction for the prognosis of COAD. COAD-related data downloaded from The Cancer Genome Atlas (TCGA) were correlated with the glycolysis process using gene set enrichment analysis (GSEA) to determine the glycolysis-related genes that regulate COAD. Using COX regression analysis, glycolysis-related genes associated with the prognosis of COAD were identified, and the genes screened to establish a predictive model. The risk scores of this model were correlated with relevant clinical data to obtain a connection diagram between the model and survival rate, tumor characteristic data, etc. Finally, genes in the model were correlated with cells in the tumor microenvironment, finding that they affected specific immune cells in the model. Seven genes related to glycolysis were identified (PPARGC1A, DLAT, 6PC2, P4HA1, STC2, ANKZF1, and GPC1), which affect the prognosis of patients with COAD and constitute the model for prediction of survival of COAD patients.

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Identification of a Novel Epithelial-Mesenchymal Transition Gene Signature Regulated by KEAP1-NRF2 Pathway in Esophageal Carcinoma

10.21203/rs.3.rs-77379/v1 ◽

2020 ◽

Author(s):

Mohamed Elshaer ◽

Ahmed Hammad ◽

Xiu Jun Wang ◽

Xiuwen Tang

Keyword(s):

Cell Lines ◽

Epithelial Mesenchymal Transition ◽

Cancer Cell Line ◽

Enrichment Analysis ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Mesenchymal Transition ◽

Gene Set ◽

Over Expression

Abstract BackgroundKEAP1-NRF2 pathway alterations were identified in many cancers including, esophageal cancer (ESCA). Identifying biomarkers that are associated with mutations in this pathway will aid in defining this cancer subset; and hence in supporting precision and personalized medicine. MethodsIn this study, 182 tumor samples from the Cancer Genome Atlas (TCGA)-ESCA RNA-Seq V2 level 3 data were segregated into two groups KEAP1-NRF2-mutated (22) and wild-type (160).The two groups were subjected to differential gene expression analysis, and we performed Gene Set Enrichment Analysis (GSEA) to determine all significantly affected biological pathways. Then, the enriched gene set was integrated with the differentially expressed genes (DEGs) to identify a gene signature regulated by the KEAP1-NRF2 pathway in ESCA. Furthermore, we validated the gene signature using mRNA expression data of ESCA cell lines provided by the Cancer Cell Line Encyclopedia (CCLE). The identified signature was tested in 3 independent ESCA datasets to assess its prognostic value.ResultsWe identified 11 epithelial-mesenchymal transition (EMT) genes regulated by the KEAP1-NRF2 pathway in ESCA patients. Five of the 11 genes showed significant over-expression in KEAP1-NRF2-mutated ESCA cell lines. In addition, the over-expression of these five genes was significantly associated with poor survival in 3 independent ESCA datasets, including the TCGA-ESCA dataset.ConclusionAltogether, we identified a novel EMT 5-gene signature regulated by the KEAP1-NRF2 axis and this signature is strongly associated with metastasis and drug resistance in ESCA. These 5-genes are potential biomarkers and therapeutic targets for ESCA patients in whom the KEAP1-NRF2 pathway is altered.

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Five lncRNAs Associated With Prostate Cancer Prognosis Identified by Coexpression Network Analysis

Technology in Cancer Research & Treatment ◽

10.1177/1533033820963578 ◽

2020 ◽

Vol 19 ◽

pp. 153303382096357

Author(s):

Xiaoyong Gong ◽

Bobin Ning

Keyword(s):

Prostate Cancer ◽

Network Analysis ◽

Cox Regression ◽

Characteristic Curve ◽

Interaction Network ◽

The Cancer Genome Atlas ◽

Cancer Prognosis ◽

Normal Prostate ◽

Cox Regression Analysis ◽

Incidence And Mortality

Prostate cancer (PCa) is a highly malignant tumor, with increasing incidence and mortality rates worldwide. The aim of this study was to identify the prognostic lncRNAs and construct an lncRNA signature for PCa diagnosis by the interaction network between lncRNAs and protein-coding genes (PCGs). The differentially expressed lncRNAs (DElncRNAs) and PCGs (DEPCGs) between PCa and normal prostate tissues were screened from The Cancer Genome Atlas (TCGA) database. The DEPCGs were functionally annotated in terms of the enriched pathways. Weighted gene co-expression network analysis (WGCNA) of 104 PCa samples identified 15 co-expression modules, of which the Turquoise module was negatively correlated with cancer and included 5 key lncRNAs and 47 PCGs. KEGG pathway analyses of the core 47 PCGs showed significant enrichment in classic PCa-related pathways, and overlapped with the enriched pathways of the DEPCGs. LINC00857, LINC00900, LINC00908, LINC00900, SNHG3 and FENDRR were significantly associated with the survival of PCa and have not been reported previously. Finally, Multivariable Cox regression analysis was used to establish a prognostic risk formula, and the patients were accordingly stratified into the low- and high-risk groups. The latter had significantly worse OS compared to the low-risk group (P < 0.01), and the area under the receiver operating characteristic curve (ROC) of 14-year OS was 0.829. The accuracy of our prediction model was determined by calculating the corresponding concordance index (C-index) and risk curves. In conclusion, we established a 5-lncRNA prognostic signature that provides insights into the biological and clinical relevance of lncRNAs in PCa.

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PVT1 is a prognostic marker associated with immune invasion of bladder urothelial carcinoma

Mathematical Biosciences and Engineering ◽

10.3934/mbe.2022009 ◽

2021 ◽

Vol 19 (1) ◽

pp. 169-190

Author(s):

Peiyuan Li ◽

◽

Gangjie Qiao ◽

Jian Lu ◽

Wenbin Ji ◽

...

Keyword(s):

Urothelial Carcinoma ◽

Immune Cell ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Gene Set Enrichment ◽

Cox Regression Analysis ◽

Gene Set ◽

Bladder Urothelial Carcinoma

<abstract> <p>Plasmacytoma variant translocation 1 (PVT1) is involved in multiple signaling pathways and plays an important regulatory role in a variety of malignant tumors. However, its role in the prognosis and immune invasion of bladder urothelial carcinoma (BLCA) remains unclear. This study investigated the expression of PVT1 in tumor tissue and its relationship with immune invasion, and determined its prognostic role in patients with BLCA. Patients were identified from the cancer genome atlas (TCGA). The enrichment pathway and function of PVT1 were explained by gene ontology (GO) term analysis, gene set enrichment analysis (GSEA) and single-sample gene set enrichment analysis (ssGSEA), and the degree of immune cell infiltration was quantified. Kaplan–Meier analysis and Cox regression were used to analyze the correlation between PVT1 and survival rate. PVT1-high BLCA patients had a lower 10-year disease-specific survival (DSS P < 0.05) and overall survival (OS P < 0.05). Multivariate Cox regression analysis showed that PVT1 (high vs. low) (P = 0.004) was an independent prognostic factor. A nomogram was used to predict the effect of PVT1 on the prognosis. PVT1 plays an important role in the progression and prognosis of BLCA and can be used as a medium biomarker to predict survival after cystectomy.</p> </abstract>

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Identification of Crucial Genes and Infiltrating Immune Cells Underlying Sepsis-Induced Cardiomyopathy via Weighted Gene Co-Expression Network Analysis

Frontiers in Genetics ◽

10.3389/fgene.2021.812509 ◽

2021 ◽

Vol 12 ◽

Author(s):

Juexing Li ◽

Lei Zhou ◽

Zhenhua Li ◽

Shangneng Yang ◽

Liangyue Tang ◽

...

Keyword(s):

Network Analysis ◽

Immune Cells ◽

Plasma Cells ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Enrichment Analysis ◽

Cardiac Metabolism ◽

Gene Set Enrichment Analysis ◽

Hub Genes ◽

Research Studies

Sepsis-induced cardiomyopathy (SIC), with a possibly reversible cardiac dysfunction, is a potential complication of septic shock. Despite quite a few mechanisms including the inflammatory mediator, exosomes, and mitochondrial dysfunction, having been confirmed in the existing research studies we still find it obscure about the overall situation of gene co-expression that how they can affect the pathological process of SIC. Thus, we intended to find out the crucial hub genes, biological signaling pathways, and infiltration of immunocytes underlying SIC. It was weighted gene co-expression network analysis that worked as our major method on the ground of the gene expression profiles: hearts of those who died from sepsis were compared to hearts donated by non-failing humans which could not be transplanted for technical reasons (GSE79962). The top 25 percent of variant genes were abstracted to identify 10 co-expression modules. In these modules, brown and green modules showed the strongest negative and positive correlation with SIC, which were primarily enriched in the bioenergy metabolism, immunoreaction, and cell death. Next, nine genes (LRRC39, COQ10A, FSD2, PPP1R3A, TNFRSF11B, IL1RAP, DGKD, POR, and THBS1) including two downregulated and seven upregulated genes which were chosen as hub genes that meant the expressive level of which was higher than the counterparts in control groups. Then, the gene set enrichment analysis (GSEA) demonstrated a close relationship of hub genes to the cardiac metabolism and the necroptosis and apoptosis of cells in SIC. Concerning immune cells infiltration, a higher level of neutrophils and B cells native and a lower level of mast cells resting and plasma cells had been observed in patients with SIC. In general, nine candidate biomarkers were authenticated as a reliable signature for deeper exploration of basic and clinical research studies on SIC.

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Identification of Genes Associated with the Metastasis of Pheochromocytoma/Paraganglioma Based on Weighted Gene Coexpression Network Analysis

BioMed Research International ◽

10.1155/2020/3876834 ◽

2020 ◽

Vol 2020 ◽

pp. 1-8 ◽

Cited By ~ 1

Author(s):

Qisheng Su ◽

Qinpei Ding ◽

Zunni Zhang ◽

Zheng Yang ◽

Yuling Qiu ◽

...

Keyword(s):

Network Analysis ◽

Enrichment Analysis ◽

Clinical Information ◽

The Cancer Genome Atlas ◽

Coexpression Network ◽

Neuroendocrine Neoplasm ◽

Gene Coexpression Network ◽

Hub Genes ◽

Gene Coexpression ◽

Coexpression Network Analysis

Background. Pheochromocytoma/paraganglioma (PCPG) is a benign neuroendocrine neoplasm in most cases, but metastasis and other malignant behaviors can be observed in this tumor. The aim of this study was to identify genes associated with the metastasis of PCPG. Methods. The Cancer Genome Atlas (TCGA) expression profile data and clinical information were downloaded from the cbioportal, and the weighted gene coexpression network analysis (WGCNA) was conducted. The gene coexpression modules were extracted from the network through the WGCNA package of R software. We further extracted metastasis-related modules of PCPG. Enrichment analysis of Biological Process of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes was carried out for important modules, and survival analysis of hub genes in the modules was performed. Results. A total of 168 PCPG samples were included in this study. The weighted gene coexpression network was constructed with 5125 genes of the top 25% variance among the 20501 genes obtained from the database. We identified 11 coexpression modules, among which the salmon module was associated with the age of PCPG patients at diagnosis, metastasis, and malignancy of the tumors. Conclusion. WGCNA was performed to identify the gene coexpression modules and hub genes in the metastasis-related gene module of PCPG. The findings in this study provide a new clue for further study of the mechanisms underlying the PCPG metastasis.

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