Identification of the Dysregulated Pathways and Key Gene in Prostate Cancer by Transcriptome Analysis and Cell Biology Experiments

Abstract Background: Prostate cancer (PCa) is a common cancer in elderly men with the first increasing new cases and the second leading cause of cancer death, but the molecular mechanisms underlying the pathogenesis of prostate cancer remain unclear. Methods: Here we mainly used Weight gene co-expression network analysis (WGCNA), Kyoto Encyclopedia of Genes and Genomes (KEGG), protein to protein interaction (PPI), gene set enrichment analysis (GSEA) and cell biology experiments to analyze prostate cancer data in GEO and The Cancer Genome Atlas (TCGA) databases and revealed the main dysregulated pathways and key genes in prostate carcinogenesis. Results: We found that the focal adhesion pathway was the main pathway in PCa. FERMT2 was shown to be the key gene for prostate tumorigenesis both in GSE6919 and TCGA datasets. By using WGCNA and GSEA analysis, we found that FERMT2 was related to the focal adhesion pathway and the ECM interaction pathway. Cell biology experiments demonstrated that FERMT2 inhibited tumor cell proliferation and migration. Conclusion: Our findings revel that downregulation of FERMT2 and the focal adhesion pathway are the main characteristics of PCa and FERMT2 might be the potential biomarker or treatment target for PCa.Trial registration: The study is not a clinical trial.

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The Role of Cancer-Associated Fibroblasts in Prostate Cancer Tumorigenesis

Cancers ◽

10.3390/cancers12071887 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1887 ◽

Cited By ~ 1

Author(s):

Francesco Bonollo ◽

George N. Thalmann ◽

Marianna Kruithof-de Julio ◽

Sofia Karkampouna

Keyword(s):

Prostate Cancer ◽

Molecular Mechanisms ◽

Therapeutic Potential ◽

Current Knowledge ◽

Therapy Resistance ◽

Cancer Associated Fibroblasts ◽

Metastatic Progression ◽

Normal Prostate ◽

Prostate Tumorigenesis

Tumors strongly depend on their surrounding tumor microenvironment (TME) for growth and progression, since stromal elements are required to generate the optimal conditions for cancer cell proliferation, invasion, and possibly metastasis. Prostate cancer (PCa), though easily curable during primary stages, represents a clinical challenge in advanced stages because of the acquisition of resistance to anti-cancer treatments, especially androgen-deprivation therapies (ADT), which possibly lead to uncurable metastases such as those affecting the bone. An increasing number of studies is giving evidence that prostate TME components, especially cancer-associated fibroblasts (CAFs), which are the most abundant cell type, play a causal role in PCa since the very early disease stages, influencing therapy resistance and metastatic progression. This is highlighted by the prognostic value of the analysis of stromal markers, which may predict disease recurrence and metastasis. However, further investigations on the molecular mechanisms of tumor–stroma interactions are still needed to develop novel therapeutic approaches targeting stromal components. In this review, we report the current knowledge of the characteristics and functions of the stroma in prostate tumorigenesis, including relevant discussion of normal prostate homeostasis, chronic inflammatory conditions, pre-neoplastic lesions, and primary and metastatic tumors. Specifically, we focus on the role of CAFs, to point out their prognostic and therapeutic potential in PCa.

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Analysis of PMEPA1 Isoforms (a and b) as Selective Inhibitors of Androgen and TGF-β Signaling Reveals Distinct Biological and Prognostic Features in Prostate Cancer

Cancers ◽

10.3390/cancers11121995 ◽

2019 ◽

Vol 11 (12) ◽

pp. 1995

Author(s):

Shashwat Sharad ◽

Zsófia M. Sztupinszki ◽

Yongmei Chen ◽

Claire Kuo ◽

Lakshmi Ravindranath ◽

...

Keyword(s):

Prostate Cancer ◽

Cell Proliferation ◽

Transmembrane Protein ◽

The Cancer Genome Atlas ◽

Cell Counting ◽

Luciferase Reporter ◽

Mrna Levels ◽

Normal Prostate ◽

Prostate Tumorigenesis ◽

Prognostic Features

Dysfunctions of androgen/TGF-β signaling play important roles in prostate tumorigenesis. Prostate Transmembrane Protein Androgen Induced 1 (PMEPA1) inhibits androgen and TGF-β signaling via a negative feedback loop. The loss of PMEPA1 confers resistance to androgen signaling inhibitors and promotes bone metastasis. Conflicting reports on the expression and biological functions of PMEPA1 in prostate and other cancers propelled us to investigate isoform specific functions in prostate cancer (PCa). One hundred and twenty laser capture micro-dissection matched normal prostate and prostate tumor tissues were analyzed for correlations between quantitative expression of PMEPA1 isoforms and clinical outcomes with Q-RT-PCR, and further validated with a The Cancer Genome Atlas (TCGA) RNA-Seq dataset of 499 PCa. Cell proliferation was assessed with cell counting, plating efficiency and soft agar assay in androgen responsive LNCaP and TGF-β responsive PC3 cells. TGF-β signaling was measured by SMAD dual-luciferase reporter assay. Higher PMEPA1-a mRNA levels indicated biochemical recurrence (p = 0.0183) and lower PMEPA1-b expression associated with metastasis (p = 0.0173). Further, lower PMEPA1-b and a higher ratio of PMEPA1-a vs. -b were correlated to higher Gleason scores and lower progression free survival rate (p < 0.01). TGF-β-responsive PMEPA1-a promoted PCa cell growth, and androgen-responsive PMEPA1-b inhibited cancer cell proliferation. PMEPA1 isoforms -a and -b were shown to be promising candidate biomarkers indicating PCa aggressiveness including earlier biochemical relapse and lower disease specific life expectancy via interrupting androgen/TGF-β signaling.

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Identification of an IRGP Signature to Predict Prognosis and Immunotherapeutic Efficiency in Bladder Cancer

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.607090 ◽

2021 ◽

Vol 8 ◽

Author(s):

Liang-Hao Zhang ◽

Long-Qing Li ◽

Yong-Hao Zhan ◽

Zhao-Wei Zhu ◽

Xue-Pei Zhang

Keyword(s):

Bladder Cancer ◽

Stromal Cells ◽

Molecular Mechanisms ◽

Proportional Hazards ◽

Enrichment Analysis ◽

Cox Proportional Hazards ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Consensus Clustering ◽

Validation Set

BackgroundIdentify immune-related gene pairs (IRGPs) signature related to the prognosis and immunotherapeutic efficiency for bladder cancer (BLCA) patients.Materials and MethodsOne RNA-seq dataset (The Cancer Genome Atlas Program) and two microarray datasets (GSE13507 and GSE31684) were included in this study. We defined these cohorts as training set to construct IRGPs and one immunotherapy microarray dataset as validation set. Identifying BLCA subclasses based on IRGPs by consensus clustering. The Lasso penalized Cox proportional hazards regression model was used to construct prognostic signature and potential molecular mechanisms were analyzed.ResultsThis signature can accurately predict the overall survival of BLCA patients and was verified in the immunotherapy validation set. IRGP-signatures can be used as independent prognostic risk factor in various clinical subgroups. Use the CIBERSORT algorithm to assess the abundance of infiltrating immune cells in each sample, and combine the results of the gene set enrichment analysis of a single sample to explore the differences in the immune microenvironment between IRPG signature groups. According to the results of GSVA, GSEA, and CIBERSORT algorithm, we found that IRGP is strikingly positive correlated with tumor microenvironment (TME) stromal cells infiltration, indicating that the poor prognosis and immunotherapy might be caused partly by enrichment of stromal cells. Finally, the results from the TIDE analysis revealed that IRGP could efficiently predict the response of immunotherapy in BLCA.ConclusionThe novel IRGP signature has a significant prognostic value for BLCA patients might facilitate personalized for immunotherapy.

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DDX10 Promotes the Proliferation and Metastasis of Colorectal Cancer Cells via Splicing RPL35

10.21203/rs.3.rs-907936/v1 ◽

2021 ◽

Author(s):

Xin Zhou ◽

Zhihong Liu ◽

Cuifeng Zhang ◽

Manman Jiang ◽

Yuxiao Jin ◽

...

Keyword(s):

Colorectal Cancer ◽

Molecular Mechanisms ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Migration And Invasion ◽

Ppi Network ◽

Interacting Protein ◽

Network Analyses ◽

Potential Biomarker

Abstract Background: Colorectal cancer (CRC) has become the second deadliest cancer in the world and severely threatens human health. An increasing number of studies have focused on the role of the RNA helicase DEAD-box (DDX) family in CRC. However, the mechanism of DDX10 in CRC has not been elucidated.Methods: In our study, we analysed the expression data of CRC samples from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Subsequently, we performed cytological experiments and animal experiments to explore the role of DDX10 in CRC cells. Furthermore, we performed Gene Ontology (GO)/ Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and protein-protein interaction (PPI) network analyses. Finally, we predicted the interacting protein of DDX10 by LC-MS/MS and verified it by coimmunoprecipitation (Co-IP) and qPCR.Results: In the present study, we identified that DDX10 mRNA was extremely highly expressed in CRC tissues compared with normal colon tissues in the TCGA and GEO databases. The protein expression of DDX10 was measured by immunochemistry (IHC) in 17 CRC patients. The biological roles of DDX10 were explored via cell and molecular biology experiments in vitro and in vivo and cell cycle assays. We found that DDX10 knockdown markedly reduced CRC cell proliferation, migration and invasion. Then, we constructed a PPI network with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). GO and KEGG enrichment analysis and gene set enrichment analysis (GSEA) showed that DDX10 was closely related to RNA splicing and E2F targets. Using LC-MS/MS and Co-IP assays, we discovered that RPL35 is the interacting protein of DDX10. In addition, we hypothesize that RPL35 is related to the E2F pathway and the immune response in CRC.Conclusions: In conclusion, provides a better understanding of the molecular mechanisms of DDX10 in CRC and provides a potential biomarker for the diagnosis and treatment of CRC.

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Identification of prostate cancer specific methylation biomarkers from a multi-cancer analysis

BMC Bioinformatics ◽

10.1186/s12859-021-04416-w ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Yiyi Pu ◽

Chao Li ◽

Haining Yuan ◽

Xiaoju Wang

Keyword(s):

Prostate Cancer ◽

Dna Methylation ◽

In Silico ◽

Prostate Cancer Screening ◽

Methylation Status ◽

The Cancer Genome Atlas ◽

Translation Rate ◽

Normal Prostate ◽

Cancer Data ◽

Significant Difference

Abstract Background Detecting prostate cancer at a non-aggressive stage is the main goal of prostate cancer screening. DNA methylation has been widely used as biomarkers for cancer diagnosis and prognosis, however, with low clinical translation rate. By taking advantage of multi-cancer data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), we aimed to identify prostate cancer specific biomarkers which can separate between non-aggressive and aggressive prostate cancer based on DNA methylation patterns. Results We performed a comparison analysis of DNA methylation status between normal prostate tissues and prostate adenocarcinoma (PRAD) samples at different Gleason stages. The candidate biomarkers were selected by excluding the biomarkers existing in multiple cancers (pan-cancer) and requiring significant difference between PRAD and other urinary samples. By least absolute shrinkage and selection operator (LASSO) selection, 8 biomarkers (cg04633600, cg05219445, cg05796128, cg10834205, cg16736826, cg23523811, cg23881697, cg24755931) were identified and in-silico validated by model constructions. First, all 8 biomarkers could separate PRAD at different stages (Gleason 6 vs. Gleason 3 + 4: AUC = 0.63; Gleason 6 vs. Gleason 4 + 3 and 8–10: AUC = 0.87). Second, 5 biomarkers (cg04633600, cg05796128, cg23523811, cg23881697, cg24755931) effectively detected PRAD from normal prostate tissues (AUC ranged from 0.88 to 0.92). Last, 6 biomarkers (cg04633600, cg05219445, cg05796128, cg23523811, cg23881697, cg24755931) completely distinguished PRAD with other urinary samples (AUC = 1). Conclusions Our study identified and in-silico validated a panel of prostate cancer specific DNA methylation biomarkers with diagnosis value.

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Identification of A Novel Six Autophagy-Related Genes Signature for The Prognostic and A miRNA-Related Autophagy Predictor for Anti-PD-1 Therapy Responses in Prostate Cancer

10.21203/rs.3.rs-42552/v1 ◽

2020 ◽

Author(s):

Lei Wu ◽

Guojun Yue ◽

Wen Quan ◽

Qiong Luo ◽

Dongxu Peng ◽

...

Keyword(s):

Prostate Cancer ◽

High Risk ◽

Regression Model ◽

Risk Group ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Differentially Expressed ◽

Prognostic Signature ◽

Potential Biomarker ◽

Risk Patients

Abstract Background: Autophagy is a highly conserved homeostatic process in the human body that is responsible for the elimination of aggregated proteins and damaged organelles. Several autophagy-related genes (ARGs) contribute to the process of tumorigenesis and metastasis of prostate cancer (PCa). Also, miRNAs have been proven to modulate autophagy by targeting some ARGs. However, their potential role in PCa still remains unclear.Methods: An univariate Cox proportional regression model was used to identify 17 ARGs associated with the overall survival (OS) of PCa. Then, a multivariate Cox proportional regression model was used to construct a 6 autophagy-related prognostic genes signature. Patients were divided into low-risk group and high-risk group using the median risk score as a cutoff value. High-risk patients had shorter OS than low-risk patients. Furthermore, the signature was validated by ROC curves. Regarding mRNA and miRNA, 12 differentially expressed miRNAs (DEMs) and 1073 differentially expressed genes (DEGs) were detected via the GEO database. We found that miR-205, one of the DEMs, was negatively regulated the expression of ARG (NKX2-3). Based on STRING analysis results, we found that the NKX2-3 was moderately related to the part of genes among the 6 autophagy-related genes prognostic signature. Further, NKX 2-3 was significantly correlated with OS and some clinical parameters of PCa by cBioProtal. By gene set enrichment analysis (GSEA). Lastly, we demonstrated that the association between NKX2-3 and tumor mutation burden (TMB) and PDCD1 (programmed cell death 1) of PCa.Results: We identified that the six ARGs expression patterns are independent predictors of OS in PCa patients. Furthermore, our results suggest that ARGs and miRNAs are inter-related. MiR-205 was negatively regulated the expression of ARG (NKX2-3). Further analysis demonstrated that NKX2-3 may be a potential biomarker for predicting the efficacy of anti-PD-1 therapy in PCa.Conclusions: The current study may offer a novel autophagy-related prognostic signature and may identify a promising miRNA-ARG pathway for predicting the efficacy of anti-PD-1 therapy in PCa.

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eIF6 Promotes the Malignant Progression of Human Hepatocellular Carcinoma via the mTOR Signaling Pathway

10.21203/rs.3.rs-78737/v1 ◽

2020 ◽

Author(s):

Liping Sun ◽

Shuguang Liu ◽

Xiaopai Wang ◽

Xuefeng Zheng ◽

Ya Chen ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Signaling Pathway ◽

Prognostic Value ◽

Molecular Mechanisms ◽

Protein Translation ◽

Translation Initiation Factor ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Initiation Factor ◽

Hcc Cells

Abstract Background Eukaryotic translation initiation factor 6 (eIF6) has a crucial function in the maturation of 60S ribosomal subunits, and it controls the initiation of protein translation. Although emerging studies indicate that eIF6 is aberrantly expressed in various types of cancers, the functions and underlying molecular mechanisms of eIF6 in the pathological progression of hepatocellular carcinoma (HCC) remain unclear. This study aimed to evaluate the potential diagnostic and prognostic value of eIF6 in patients with HCC. Methods HCC samples enrolled from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and our cohort were used to explore the role and mechanism of eIF6 in HCC. The diagnostic power of eIF6 was verified by receiver operating characteristic curve (ROC) analysis and its prognostic value was assessed by Kaplan-Meier analysis, and then related biological functions of eIF6 were determined in vitro and in vivo cancer models. In addition, potential molecular mechanism of eIF6 in HCC was unveiled by the gene set enrichment analysis and western blot assay. Results We demonstrated that eIF6 expression was markedly increased in HCC, and elevated eIF6 expression correlated with pathological progression of HCC. Besides, eIF6 served as not only a new diagnostic biomarker but also an independent risk factor for OS in HCC patients. Functional studies indicated that the deletion of eIF6 displayed tumor-suppressor activity in HCC cells. Furthermore, we found that eIF6 could activate the mTOR-related signaling pathway and regulate the expression level of its target genes, such as CCND1, CDK4, CDK6, MYC, CASP3 and CTNNBL1, and these activities promoted proliferation and invasion of HCC cells. Conclusions The findings of this study provided a novel basis for understanding the potential role of eIF6 in promoting tumor growth and invasion, and exploited a promising strategy for improving diagnosis and prognosis of HCC.

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FBLN5 Is an Underlying Common Tumor Suppressor in Breast Cancer and Thyroid Cancer

10.21203/rs.3.rs-1162202/v1 ◽

2022 ◽

Author(s):

Zhao-min XIE ◽

Ying-sheng XIAO ◽

Chun-yan XU ◽

Qin XIE ◽

Wen-de WANG ◽

...

Keyword(s):

Breast Cancer ◽

Thyroid Cancer ◽

Tumor Suppressor ◽

Molecular Mechanisms ◽

Differential Expression Analysis ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Prognostic Gene ◽

Hormone Biosynthesis ◽

Key Genes

Abstract Background: Breast cancer (BC) patients have a greater risk of developing thyroid cancer (TC) than the general population. Similarly, TC patients are more likely to develop BC, suggesting an underlying common etiology. In this study, we sought to identify the potential cross-talking pathway and related molecular mechanisms conferring to the sequential development of BC and TC.Methods: We first used Multiple Primary-Standardized Incidence Ratios (MP-SIR) Program of SEER*Stat to calculate SIR to confirm the relationship between BC and TC. Then the RNA-seq was downloaded from The Cancer Genome Atlas (TCGA). And we built a co-expression network via Weighted Gene Co-expression Network Analysis (WGCNA) and obtained the most significant modules. The key genes were obtained by differential gene expression (DGE) analysis and WGCNA analysis. Furthermore, String database and Cytoscape software were used to construct protein-protein interactions (PPI), and defined the maximum Maximal Clique Centrality (MCC) value as hub gene.Then we performed prognosis analysis on the hub genes and obtained the prognostic genes of BC and TC. Finally, gene set enrichment analysis (GSEA) was used to investigate the molecular pathways associated with prognostic gene expressed both in BC and TC.Results: From the SEER database, we found that the risk of developing BC in TC patients was SIR 1.12, 95% CI [1.07, 1.18], and the risk of developing BC in TC patients was SIR 1.29, 95% CI [1.23, 1.26]. Fifty-nine key genes obtained by differential expression analysis and WGCNA identify that PI3K/AKT was the most enriched pathway in BC and TC. In addition, the Recombinant Fibulin 5 (FBLN5) was shown to be of significant prognostic value for both BC and TC and was down-regulated in BC and TC tissues. GSEA demonstrated that FBLN5 enrichment pathways associated with BC and TC mainly included: B cell receptor signaling pathway, steroid hormone biosynthesis, and pathways in cancer.Conclusions: The PI3K/AKT signaling is most co-enriched pathway in BC and TC. FBLN5 is the most relevant prognostic gene and an underlying common tumor suppressor in both BC and TC, with down-stream pathways involving immunity, hormone biosynthesis and carcinogenesis.

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Active vitamin D induces gene-specific hypomethylation in prostate cancer cells developing vitamin D resistance

AJP Cell Physiology ◽

10.1152/ajpcell.00522.2019 ◽

2020 ◽

Vol 318 (5) ◽

pp. C836-C847 ◽

Cited By ~ 2

Author(s):

Guan-Rong Lai ◽

Yi-Fen Lee ◽

Shian-Jang Yan ◽

Huei-Ju Ting

Keyword(s):

Prostate Cancer ◽

Vitamin D ◽

Cancer Progression ◽

Transcriptional Activation ◽

Cell Model ◽

In Silico Analysis ◽

Dna Methyltransferases ◽

The Cancer Genome Atlas ◽

Data Set ◽

Cancer Data

Prostate cancer (PCa) is a leading cause of cancer death in men. Despite the antiproliferative effects of 1α,25-dihydroxyvitamin D3 [1,25(OH)2D3] on PCa, accumulating evidence indicates that 1,25(OH)2D3 promotes cancer progression by increasing genome plasticity. Our investigation of epigenetic changes associated with vitamin D insensitivity found that 1,25(OH)2D3 treatment reduced the expression levels and activities of DNA methyltransferases 1 and 3B (DNMT1 and DNMT3B, respectively). In silico analysis and reporter assay confirmed that 1,25(OH)2D3 downregulated transcriptional activation of the DNMT3B promoter and upregulated microRNAs targeting the 3′-untranslated regions of DNMT3B. We then profiled DNA methylation in the vitamin D-resistant PC-3 cells and a resistant PCa cell model generated by long-term 1,25(OH)2D3 exposure. Several candidate genes were found to be hypomethylated and overexpressed in vitamin D-resistant PCa cells compared with vitamin D-sensitive cells. Most of the identified genes were associated with mammalian target of rapamycin (mTOR) signaling activation, which is known to promote cancer progression. Among them, we found that inhibition of ribosomal protein S6 kinase A1 (RPS6KA1) promoted vitamin D sensitivity in PC-3 cells. Furthermore, The Cancer Genome Atlas (TCGA) prostate cancer data set demonstrated that midline 1 ( MID1) expression is positively correlated with tumor stage. Overall, our study reveals an inhibitory mechanism of 1,25(OH)2D3 on DNMT3B, which may contribute to vitamin D resistance in PCa.

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Identification of Potential Biomarkers and Immune Features of Sepsis Using Bioinformatics Analysis

Mediators of Inflammation ◽

10.1155/2020/3432587 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Fang-Chen Gong ◽

Ran Ji ◽

Yu-Ming Wang ◽

Zhi-Tao Yang ◽

Ying Chen ◽

...

Keyword(s):

Cell Biology ◽

Molecular Mechanisms ◽

Cell Function ◽

Killer Cell ◽

Functional Enrichment ◽

Data Repository ◽

Bioinformatics Analyses ◽

Potential Biomarker ◽

New Biomarkers ◽

Potential Biomarkers

Sepsis remains a major global concern and is associated with high mortality and morbidity despite improvements in its management. Markers currently in use have shortcomings such as a lack of specificity and failures in the early detection of sepsis. In this study, we aimed to identify key genes involved in the molecular mechanisms of sepsis and search for potential new biomarkers and treatment targets for sepsis using bioinformatics analyses. Three datasets (GSE95233, GSE57065, and GSE28750) associated with sepsis were downloaded from the public functional genomics data repository Gene Expression Omnibus. Differentially expressed genes (DEGs) were identified using R packages (Affy and limma). Functional enrichment of the DEGs was analyzed with the DAVID database. Protein-protein interaction networks were derived using the STRING database and visualized using Cytoscape software. Potential biomarker genes were analyzed using receiver operating characteristic (ROC) curves in the R package (pROC). The three datasets included 156 whole blood RNA samples from 89 sepsis patients and 67 healthy controls. Between the two groups, 568 DEGs were identified, among which 315 were upregulated and 253 were downregulated in the septic group. These genes were enriched for pathways mainly involved in the innate immune response, T-cell biology, antigen presentation, and natural killer cell function. ROC analyses identified nine genes—LRG1, ELANE, TP53, LCK, TBX21, ZAP70, CD247, ITK, and FYN—as potential new biomarkers for sepsis. Real-time PCR confirmed that the expression of seven of these genes was in accordance with the microarray results. This study revealed imbalanced immune responses at the transcriptomic level during early sepsis and identified nine genes as potential biomarkers for sepsis.

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