Identification of an oncogenic network with prognostic and therapeutic value in prostate cancer

The yin and yang of chromosomal instability in prostate cancer

Metastatic prostate cancer remains an incurable lethal disease. Studies indicate that prostate cancer accumulates genomic changes during disease progression and displays the highest levels of chromosomal instability (CIN) across all types of metastatic tumours. CIN, which refers to ongoing chromosomal DNA gain or loss during mitosis, and derived aneuploidy, are known to be associated with increased tumour heterogeneity, metastasis and therapy resistance in many tumour types. Paradoxically, high CIN levels are also proposed to be detrimental to tumour cell survival, suggesting that cancer cells must develop adaptive mechanisms to ensure their survival. In the context of prostate cancer, studies indicate that CIN has a key role in disease progression and might also offer a therapeutic vulnerability that can be pharmacologically targeted. Thus, a comprehensive evaluation of the causes and consequences of CIN in prostate cancer, its contribution to aggressive advanced disease and a better understanding of the acquired CIN tolerance mechanisms can translate into new tumour classifications, biomarker development and therapeutic strategies.

Systematic review and integrated analysis of prognostic gene signatures for prostate cancer patients

Prostate cancer (PC) is one of the most common cancers in men and becoming the second leading cause of cancer fatalities. At present, the lack of effective strategies for prognosis of PC patients is still a problem to be solved. Therefore, it is significant to identify potential gene signatures for PC patients' prognosis. Here, we summarized 71 different prognostic gene signatures for PC and concluded 3 strategies for signature construction after extensive investigation. In addition, 14 genes frequently appeared in 71 different gene signatures, which enriched in mitotic and cell cycle. This review provides extensive understanding and integrated analysis of current prognostic signatures of PC, which may help researchers to construct gene signatures of PC and guide future clinical treatment.

Automation, live-cell imaging, and endpoint cell viability for prostate cancer drug screens

Androgen deprivation therapy (ADT) is the standard of care for high risk and advanced prostate cancer; however, disease progression from androgen-dependent prostate cancer (ADPC) to lethal and incurable castration-resistant prostate cancer (CRPC) and (in a substantial minority of cases) neuroendocrine prostate cancer (NEPC) is common. Identifying effective targeted therapies is challenging because of acquired resistance to established treatments and the vast heterogeneity of advanced prostate cancer (PC). To streamline the identification of potentially active prostate cancer therapeutics, we have developed an adaptable semi-automated protocol which optimizes cell growth and leverages automation to enhance robustness, reproducibility, and throughput while integrating live-cell imaging and endpoint viability assays to assess drug efficacy in vitro. In this study, culture conditions for 72-hr drug screens in 96-well plates were established for a large, representative panel of human prostate cell lines including: BPH-1 and RWPE-1 (non-tumorigenic), LNCaP and VCaP (ADPC), C4-2B and 22Rv1 (CRPC), DU 145 and PC3 (androgen receptor-null CRPC), and NCI-H660 (NEPC). The cell growth and 72-hr confluence for each cell line was optimized for real-time imaging and endpoint viability assays prior to screening for novel or repurposed drugs as proof of protocol validity. We demonstrated effectiveness and reliability of this pipeline through validation of the established finding that the first-in-class BET and CBP/p300 dual inhibitor EP-31670 is an effective compound in reducing ADPC and CRPC cell growth. In addition, we found that insulin-like growth factor-1 receptor (IGF-1R) inhibitor linsitinib is a potential pharmacological agent against highly lethal and drug-resistant NEPC NCI-H660 cells. This protocol can be employed across other cancer types and represents an adaptable strategy to optimize assay-specific cell growth conditions and simultaneously assess drug efficacy across multiple cell lines.

Inhibition of the serine/threonine kinase BUB1 reverses taxane resistance in prostate cancer

Men with incurable castration resistant prostate cancer (CRPC) are typically treated with taxanes; however, drug resistance rapidly develops. We previously identified a clinically relevant seven gene network in aggressive CRPC, which includes the spindle assembly checkpoint (SAC) kinase BUB1. Since SAC is deregulated in taxane resistant PC, we evaluated BUB1 and found that it was over-expressed in advanced PC patient datasets and taxane resistant PC cells. Treatment with a specific BUB1 kinase inhibitor re-sensitized resistant CRPC cells, including cells expressing constitutively active androgen receptor (AR) variants, to clinically used taxanes. Consistent with a role of AR variants in taxane resistance, ectopically expressed AR-V7 increased BUB1 levels and reduced sensitivity to taxanes. This work shows that disruption of BUB1 kinase activity reverted resistance to taxanes, which is essential to advancing BUB1 as a potential therapeutic target for intractable chemotherapy resistant CRPC including AR variant driven CRPC, which lacks durable treatment options.

Harnessing transcriptionally driven chromosomal instability adaptation to target therapy-refractory lethal prostate cancer

Metastatic prostate cancer (PCa) inevitably acquires resistance to standard therapy preceding lethality. Here, we unveil a chromosomal instability (CIN) tolerance mechanism as a therapeutic vulnerability of therapy-refractory lethal PCa. Through genomic and transcriptomic analysis of patient datasets, we find that castration and chemotherapy-resistant tumors display the highest CIN and mitotic kinase levels. Functional genomics screening coupled with quantitative phosphoproteomics identify MASTL kinase as a survival vulnerability specific of chemotherapy-resistant PCa cells. Mechanistically, MASTL upregulation is driven by transcriptional rewiring mechanisms involving the non-canonical transcription factors androgen receptor splice variant 7 and E2F7 in a circuitry that restrains deleterious CIN and prevents cell death selectively in metastatic therapy-resistant PCa cells. Notably, MASTL pharmacological inhibition re-sensitizes tumors to standard therapy and improves survival of pre-clinical models. These results uncover a targetable mechanism promoting high CIN adaptation and survival of lethal PCa.

The kinesin KIF20A promotes progression to castration-resistant prostate cancer through autocrine activation of the androgen receptor

Prostate cancer that recurs following androgen-deprivation therapy is termed castration-resistant, which is incurable and is marked by reactivation of androgen receptor (AR) signaling. KIF20A, a kinesin with unique structural features, is overexpressed in human castration-resistant prostate cancer (CRPC) compared to androgen-dependent PC and benign tissue. KIF20A has well-described roles in mitotic processes, but it has a less characterized function in vesicle fission and trafficking within Golgi-driven secretory pathways. Stable expression of KIF20A in androgen-dependent PC cells promoted progression to CRPC through the activation of AR signaling in vitro and in vivo. KIF20A expression resulted in the secretion of autocrine factors in the conditioned media that activated AR and caused castration-resistant proliferation of naïve androgen-dependent cells. Pharmacologic disruption of vesicle biogenesis blocked KIF20A-driven castration-resistant proliferation of androgen-dependent PC. KIF20A depletion or treatment with the KIF20A-specific inhibitor, paprotrain, reduced CRPC. These data are the first to establish KIF20A as a driver of CRPC progression through AR activation and as a promising therapeutic target against CRPC.

Molecular Characteristics and Prognostic Role of MFAP2 in Stomach Adenocarcinoma

Molecular characteristics and prognostic role of MFAP2 were by no means stated. The MFAP2 expression and prognostic prices in this study, with Cox analysis, was employed to develop a predictive fee for MFAP2. To know about coexpression and practical networks associated with MFAP2, LinkedOmics and GEPIA2 have been used. MFAP2 expression has been increased and verified in many unbiased coalitions in TCGA-STAD tumor tissues. In addition, in each TCGA and various cohorts, increased MFAP2 was linked with lower survival. Evaluation by Cox revealed the unbiased danger to average survival, disease-specific survival, and progression-free survival of STAD used to be due to the elevated expression of MFAP2. Active community assessed the MFAP2, through which more than a few cancer-associated kinases and E2F household pathways are regulated, which shows that MFAP2 affects RNA transportation, oocyte meiosis, spliceosome, and ribosome biogenesis. MFAP2 can predict and is linked to the prediction of STAD independently. The closure of the MFAP2 link to the macrophage marker genes is, in particular, the achievable core of immune response.

A Motif-Based Network Analysis of Regulatory Patterns in Doxorubicin Effects on Treating Breast Cancer, a Systems Biology Study

Background

Breast cancer is the most common malignancy worldwide. Doxorubicin is an anthracycline used to treat breast cancer as the first treatment choice. Nevertheless, the molecular mechanisms underlying the response to Doxorubicin and its side effects are not comprehensively understood so far. We used systems biology and bioinformatics methods to identify essential genes and molecular mechanisms behind the body response to Doxorubicin and its side effects in breast cancer patients.

Methods

Omics data were extracted and analyzed to construct the protein-protein interaction and gene regulatory networks. Network analysis was performed to identify hubs, bottlenecks, clusters, and regulatory motifs to evaluate crucial genes and molecular mechanisms behind the body response to Doxorubicin and its side effects.

Results

Analyzing the constructed PPI and gene-TF-miRNA regulatory network showed that MCM3, MCM10, and TP53 are key hub-bottlenecks and seed proteins. Enrichment analysis also revealed cell cycle, TP53 signaling, Forkhead box O (FoxO) signaling, and viral carcinogenesis as essential pathways in response to this drug. Besides, SNARE interactions in vesicular transport and neurotrophin signaling were identified as pathways related to the side effects of Doxorubicin. The apoptosis induction, DNA repair, invasion inhibition, metastasis, and DNA replication are suggested as critical molecular mechanisms underlying Doxorubicin anti-cancer effect. SNARE interactions in vesicular transport and neurotrophin signaling and FoxO signaling pathways in glucose metabolism are probably the mechanisms responsible for side effects of Doxorubicin.

Conclusion

Following our model validation using the existing experimental data, we recommend our other newly predicted biomarkers and pathways as possible molecular mechanisms and side effects underlying the response to Doxorubicin in breast cancer requiring further investigations.

Identification of proteins associated with treatment response of neoadjuvant chemoradiotherapy in rectal mucinous adenocarcinoma by co-expression network analysis based on proteomic analysis

For rectal mucinous adenocarcinoma (MAC), identifying biomarkers of neoadjuvant chemoradiotherapy (NCRT) response has become imperative. This study applied label-free mass spectrometry and weighted gene co-expression network analysis to identify hub proteins in association with the NCRT response in 20 rectal MAC patients. We identified 131 differentially abundant proteins and 7 candidate proteins associated with the NCRT response. The immunostaining expressions of six proteins (ENOA, ILEU, MDHM, RM11, PTGDS, and RL3) were significantly associated with the NCRT response. Logistic regression analysis revealed that ENOA (OR = 6.275, P = 0.006) was independent risk hub protein for the NCRT response. Tow hub proteins (ENOA and PTGDS) were identified as significant risk factors by Cox regression analysis. A prognostic risk score system was constructed: risk score = (0.910 × EXP_ENOA) + (-1.519 × EXP_PTGDS), and found to be an independent predictor of DFS in rectal MAC patients (HR = 10.308, P < 0.001). Our study suggested that ENOA may be a novel biomarker for the NCRT response and prognosis in rectal MAC patients. A two-hub-protein-based risk score system might be used for predicting tumor recurrence in rectal MAC patients. SIGNIFICANCE: NCRT resistance is a major problem in the treatment of rectal MAC patients. Identifying robust predictive biomarkers for NCRT resistance is beneficial to the stratified treatment of rectal MAC patients. In this study, label-free mass spectrometry and weighted gene co-expression network analysis identified ENOA as a potential novel biomarker for the NCRT response and prognosis. ENOA may be involved in the process of the NCRT resistance and tumor recurrence through the carbon metabolism pathway.

Multi-omics analysis defines 5-fluorouracil drug resistance in 3D HeLa carcinoma cell model

Cervical cancer is a serious health problem in women around the globe. However, the use of clinical drug is seriously dampened by the development of drug resistance. Efficient in vitro tumor model is essential to improve the efficiency of drug screening and the accuracy of clinical application. Multicellular tumor spheroids (MTSs) can in a way recapitulates tumor traits in vivo, thereby representing a powerful transitional model between 2D monolayer culture and xenograft. In this study, based on the liquid overlay method, a protocol for rapid generation of the MTSs with uniform size and high reproducibility in a high-throughput manner was established. As expected, the cytotoxicity results showed that there was enhanced 5-fluorouracil (5-FU) resistance of HeLa carcinoma cells in 3D MTSs than 2D monolayer culture with a resistance index of 5.72. In order to obtain a holistic view of the molecular mechanisms that drive 5-FU resistance in 3D HeLa carcinoma cells, a multi-omics study was applied to discover hidden biological regularities. It was observed that in the 3D MTSs mitochondrial function-related proteins and the metabolites of the tricarboxylic acid cycle (TCA cycle) were significantly decreased, and the cellular metabolism was shifted towards glycolysis. The differences in the protein synthesis, processing, and transportation between 2D monolayer cultures and 3D MTSs were significant, mainly in the heat shock protein family, with the up-regulation of protein folding function in endoplasmic reticulum (ER), which promoted the maintenance of ER homeostasis in the 3D MTSs. In addition, at the transcript and protein level, the expression of extracellular matrix (ECM) proteins (e.g., laminin and collagen) were up-regulated in the 3D MTSs, which enhanced the physical barrier of drug penetration. Summarizing, this study formulates a rapid, scalable and reproducible in vitro model of 3D MTS for drug screening purposes, and the findings establish a critical role of glycolytic metabolism, ER hemostasis and ECM proteins expression profiling in tumor chemoresistance of HeLa carcinoma cells towards 5-FU.

Hypermethylation and Downregulation of UTP6 Are Associated With Stemness Properties, Chemoradiotherapy Resistance, and Prognosis in Rectal Cancer: A Co-expression Network Analysis

Background

To identify the hub genes associated with chemoradiotherapy resistance in rectal cancer and explore the potential mechanism.

Methods

Weighted gene co-expression network analysis (WGCNA) was performed to identify the gene modules correlated with the chemoradiotherapy resistance of rectal cancer.

Results

The mRNA expression of 31 rectal cancer patients receiving preoperative chemoradiotherapy was described in our previous study. Through WGCNA, we demonstrated that the chemoradiotherapy resistance modules were enriched for translation, DNA replication, and the androgen receptor signaling pathway. Additionally, we identified and validated UTP6 as a new effective predictor for chemoradiotherapy sensitivity and a prognostic factor for the survival of colorectal cancer patients using our data and the GSE35452 dataset. Low UTP6 expression was correlated with significantly worse disease-free survival (DFS), overall survival (OS), and event- and relapse-free survival both in our data and the R2 Platform. Moreover, we verified the UTP6 expression in 125 locally advanced rectal cancer (LARC) patients samples by immunohistochemical analysis. The results demonstrated that low UTP6 expression was associated with worse DFS and OS by Kaplan-Meier and COX regression model analyses. Gene set enrichment and co-expression analyses showed that the mechanism of the UTP6-mediated chemoradiotherapy resistance may involve the regulation of FOXK2 expression by transcription factor pathways.

Conclusion

Low expression of the UTP6 was found to be associated with chemoradiotherapy resistance and the prognosis of colorectal cancer possibly via regulating FOXK2 expression by transcription factor pathways.

Identification of LncRNAs Associated With FOLFOX Chemoresistance in mCRC and Construction of a Predictive Model

Oxaliplatin, fluorouracil plus leucovorin (FOLFOX) regimen is the first-line chemotherapy of patients with metastatic colorectal cancer (mCRC). However, studies are limited regarding long non-coding RNAs (lncRNAs) associated with FOLFOX chemotherapy response and prognosis. This study aimed to identify lncRNAs associated with FOLFOX chemotherapy response and prognosis in mCRC patients and to construct a predictive model. We analyzed lncRNA expression in 11 mCRC patients treated with FOLFOX chemotherapy before surgery (four sensitive, seven resistant) by Gene Array Chip. The top eight lncRNAs (AC007193.8, CTD-2008N3.1, FLJ36777, RP11-509J21.4, RP3-508I15.20, LOC100130950, RP5-1042K10.13, and LINC00476) for chemotherapy response were identified according to weighted correlation network analysis (WGCNA). A competitive endogenous RNA (ceRNA) network was then constructed. The crucial functions of the eight lncRNAs enriched in chemotherapy resistance were mitogen-activated protein kinase (MAPK) and proteoglycans signaling pathway. Receiver operating characteristic (ROC) analysis demonstrated that the eight lncRNAs were potent predictors for chemotherapy resistance of mCRC patients. To further identify a signature model lncRNA chemotherapy response and prognosis, the validation set consisted of 196 CRC patients from our center was used to validate lncRNAs expression and prognosis by quantitative PCR (qPCR). The expression of the eight lncRNAs expression between CRC cancerous and adjacent non-cancerous tissues was also verified in the validation data set to determine the prognostic value. A generalized linear model was established to predict the probability of chemotherapy resistance and survival. Our findings showed that the eight-lncRNA signature may be a novel biomarker for the prediction of FOLFOX chemotherapy response and prognosis of mCRC patients.

A detailed characterization of stepwise activation of the androgen receptor variant 7 in prostate cancer cells

Expression of the andrgogen receptor splice variant 7 (AR-V7) is frequently detected in castrate resistant prostate cancer and associated with resistance to AR-targeted therapies. While we have previously noted that homodimerization is required for the transcriptional activity of AR-V7 and that AR-V7 can also form heterodimers with the full-length AR (AR-FL), there are still many gaps of knowledge in AR-V7 stepwise activation. In the present study, we show that neither AR-V7 homodimerization nor AR-V7/AR-FL heterodimerization requires cofactors or DNA binding. AR-V7 can enter the nucleus as a monomer and drive a transcriptional program and DNA-damage repair as a homodimer. While forming a heterodimer with AR-FL to induce nuclear localization of unliganded AR-FL, AR-V7 does not need to interact with AR-FL to drive gene transcription or DNA-damage repair in prostate cancer cells that co-express AR-V7 and AR-FL. These data indicate that AR-V7 can function independently of its interaction with AR-FL in the true castrate state or “absence of ligand”, providing support for the utility of targeting AR-V7 in improving outcomes of patients with castrate resistant prostate cancer.

Association study between genetic variants in retinol metabolism pathway genes and prostate cancer risk

Background

Evidence suggests that serum retinol level is associated with prostate cancer risk, but the association between genetic variants in the retinol metabolism pathway genes and prostate cancer risk remains unclarified.

Methods

Single‐nucleotide polymorphisms (SNPs) in 31 genes in the retinol metabolism pathway were genotyped to evaluate the association with prostate cancer risk in 4,662 cases and 3,114 controls from the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial. The gene expression analysis was evaluated using data from the Gene Expression Omnibus (GEO) datasets and the Cancer Genome Atlas (TCGA) database. Data from the Genotype‐Tissue Expression (GTEx) project dataset were utilized to perform the expression quantitative trait loci (eQTL) analysis.

Results

Two SNPs were significantly associated with prostate cancer risk [rs1330286 in ALDH1A1: odds ratio (OR) = 0.88, 95% confidence interval (CI) = 0.83‐0.94, p = 2.45 × 10⁻⁴; rs4646653 in ALDH1A3: OR = 1.17, 95% CI =1.07‐1.27, p = 4.33 × 10⁻⁴]. Moreover, the mRNA level of ALDH1A3 was significantly higher in prostate cancer tissues than in normal tissues in both TCGA datasets and GEO datasets (p = 1.63 × 10⁻¹² and p = 4.33 × 10⁻², respectively). rs1330286 was an eQTL of ALDH1A1 (P = 2.90 × 10⁻³).

Conclusion

Our findings highlight that genetic variants in retinol metabolism pathway genes are associated with prostate cancer risk.

A Four Gene-Based Risk Score System Associated with Chemoradiotherapy Response and Tumor Recurrence in Rectal Cancer by Co-Expression Network Analysis

Aim

Resistance to neoadjuvant chemoradiotherapy (NCRT) and tumor recurrence presents a major clinical problem in locally advanced rectal cancer (LARC) patients. This study aimed to explore a genetic risk score related to NCRT response and tumor recurrence in rectal cancer after NCRT.

Materials and Methods

Weighted gene co-expression network analysis was employed to identify hub genes associated with NCRT response from the GSE93375 dataset. Prognostic hub genes were determined using Cox regression analysis and associated with disease-free survival (DFS). A risk score system was constructed and the prognostic significance of the risk score was validated in our patient cohort. A predictive nomogram for DFS was developed and validated internally.

Results

The Tan module had the highest correlations with NCRT response. Ten hub genes (COL15A1, THBS2, ITGB1, MMP2, CD34, SPARC, NOTCH3, PDGFRB, DCN, and SERPINH1) were associated with NCRT response. Immunostaining expression of four genes (NOTCH3, SPARC, DCN, and ITGB1) was found to be significantly associated with both NCRT response and DFS in our patient cohort and was selected to build a prognostic risk score for DFS as follows: risk score= (0.6188×Exp _NOTCH3 ) + (0.6511×Exp _SPARC ) + (-0.2976×Exp _DCN ) + (1.0035×Exp _ITGB1 ). Using this risk score, patients could be separated into high- and low-risk groups for tumor recurrence. A nomogram that incorporated the risk score, ypTNM stage, and tumor regression grade (TRG) was constructed and utilized to predict DFS in LARC patients.

Conclusion

The four-gene expression-based risk score system presented here could be potentially used for predicting tumor recurrence in LARC patients after NCRT.

FBXW4 Acts as a Protector of FOLFOX-Based Chemotherapy in Metastatic Colorectal Cancer Identified by Co-Expression Network Analysis

Background

FOLFOX chemotherapy is one of the most commonly used treatments for colorectal cancer (CRC) patients. However, the efficacy and tolerance of FOLFOX therapy varies between patients. The purpose of this study was to explore hub genes associated with primary chemotherapy-resistance and to explore the possible mechanisms involved from non-European patients.

Method

A weighted gene co-expression network was constructed to identify gene modules associated with chemotherapy resistance in mCRC from China.

Results

A Gene Array Chip was used to detect mRNA expression in 11 mCRC patients receiving preoperative FOLFOX chemotherapy. The immune response was associated with chemotherapy-resistance in microarray data. Through the use of WGCNA, we demonstrated that the crucial functions enriched in chemotherapy-resistance modules were cell proliferation, MAPK signaling pathways, and PI3K signaling pathways. Additionally, we identified and validated FBXW4 as a new effective predictor for chemotherapy sensitivity and a prognostic factor for survival of CRC patients by using our own data and GSE69657. Furthermore, a meta-analysis of 15 Gene Expression Omnibus–sourced datasets showed that FBXW4 messenger RNA levels were significantly lower in CRC tissues than in normal colon tissues. An analysis of the data from the R2: Genomics Analysis and Visualization Platform showed that low FBXW4 expression was correlated with a significantly worse event- and relapse-free survival. Gene set enrichment analysis showed that the mechanism of FBXW4-mediated chemotherapy resistance may involve the DNA replication signal pathway and the cell cycle.

Conclusion

FBXW4 is associated with chemotherapy resistance and prognosis of CRC probably by regulating DNA replication signaling pathways and the cell cycle.

Oncogenic Network and Hub Genes for Natural Killer/T-Cell Lymphoma Utilizing WGCNA

Natural killer (NK)/T-cell lymphoma (NKTCL) is a subtype of non-Hodgkin lymphoma with aggressive progression and poor prognosis. The molecular mechanisms of NKTCL have not been well-studied. Herein, we revealed the lymphoma-associated dysregulated genes and signaling pathways or biological processes in NKTCL. We characterized that the extracellular matrix (ECM) receptor interaction pathway and T-cell receptor signaling pathway were the main dysregulated pathways in NKTCL by Gene Ontology (GO) analysis and pathway enrichment analysis. By using weighted gene co-expression network analysis (WGCNA), the gene co-expression network of NKTCL (SRP049695) was constructed, and hub genes (LMO3, GRB14) were identified. In addition, another Gene Expression Omnibus (GEO) dataset (GSE69406) was used to validate these hub genes. Furthermore, these hub genes were identified and validated by survival analysis (GSE90597). These results provided novel insights into the pathogenesis of NKTCL. Of particular interest, LMO3 and GRB14 might be potential oncoproteins and biomarkers for the diagnosis and treatment of NKTCL.

Androgen receptor variant-driven prostate cancer II: advances in laboratory investigations

Background:

The androgen receptor (AR) is a key prostate cancer drug target. Suppression of AR signaling mediated by the full-length AR (AR-FL) is the therapeutic goal of all existing AR-directed therapies. AR-targeting agents impart therapeutic benefit, but lead to AR aberrations that underlie disease progression and therapeutic resistance. Among the AR aberrations specific to castration-resistant prostate cancer (CRPC), AR variants (AR-Vs) have emerged as important indicators of disease progression and therapeutic resistance.

Methods:

We conducted a systemic review of the literature focusing on recent laboratory studies on AR-Vs following our last review article published in 2016. Topics ranged from measurement and detection, molecular origin, regulation, genomic function, and preclinical therapeutic targeting of AR-Vs. We provide expert opinions and perspectives on these topics.

Results:

Transcript sequences for 22 AR-Vs have been reported in the literature. Different AR-Vs may arise through different mechanisms, and can be regulated by splicing factors and dictated by genomic rearrangements, but a low-androgen environment is a prerequisite for generation of AR-Vs. The unique transcript structures allowed development of in-situ and in-solution measurement and detection methods, including mRNA and protein detection, in both tissue and blood specimens. AR variant-7 (AR-V7) remains the main measurement target and the most extensively characterized AR-V. Although AR-V7 co-exists with AR-FL, genomic functions mediated by AR-V7 do not require the presence of AR-FL. The distinct cistromes and transcriptional programs directed by AR-V7 and their co-regulators are consistent with genomic features of progressive disease in a low-androgen environment. Preclinical development of AR-V-directed agents currently focuses on suppression of mRNA expression and protein degradation as well as targeting of the amino-terminal domain.

Conclusions:

Current literature continues to support AR-Vs as biomarkers and therapeutic targets in prostate cancer. Laboratory investigations reveal both challenges and opportunities in targeting AR-Vs to overcome resistance to current AR-directed therapies.

Expression profile-based screening for critical genes reveals S100A4, ACKR3 and CDH1 in docetaxel-resistant prostate cancer cells

Docetaxel is a first-line anticancer drug widely used in the treatment of advanced prostate cancer. However, its therapeutic efficacy is limited by its side effects and the development of chemoresistance by the tumor. Using a gene differential expression microarray, we identified 449 genes differentially expressed in docetaxel-resistant DU145 and PC3 cell lines as compared to docetaxel-sensitive controls. Moreover, western blotting and immunohistochemistry revealed altered expression of S100A4, ACKR3 and CDH1in clinical tumor samples. Cytoscape software was used to investigate the relationship between critical proteins and their signaling transduction networks. Functional and pathway enrichment analyses revealed that these signaling pathways were closely related to cellular proliferation, cell adhesion, cell migration and metastasis. In addition, ACKR3 knockout using the crispr/cas9 method andS100A4knockdownusing targeted shRNA exerted additive effects suppressing cancer cell proliferation and migration. This exploratory analysis provides information about potential candidate genes. It also provides new insight into the molecular mechanism underlying docetaxel-resistance in androgen-independent prostate cancer and highlights potential targets to improve therapeutic outcomes.

Integrated profiling identifies SLC5A6 and MFAP2 as novel diagnostic and prognostic biomarkers in gastric cancer patients

Gastric cancer (GC) is one of the leading causes of malignancy‑associated mortality worldwide. However, the underlying molecular mechanisms of GC are unclear and the prognosis of GC is poor. Therefore, it is important and urgent to explore the underlying mechanisms and screen for novel diagnostic and prognostic biomarkers, as well as therapeutic targets. In the current study, scale‑free gene co‑expression networks were constructed using weighted gene co‑expression network analysis, the potential associations between gene sets and clinical features were investigated, and the hub genes were identified. The gene expression profiles of GSE38749 were downloaded from the Gene Expression Omnibus database. RNA‑seq and clinical data for GC from The Cancer Genome Atlas were utilized for verification. Furthermore, the expression of candidate biomarkers in gastric tissues was investigated. Survival analysis was performed using Kaplan‑Meier and log‑rank test. The predictive role of candidate biomarkers in GC was evaluated using a receiver operator characteristic (ROC) curve. Gene Ontology, gene set enrichment analysis and gene set variation analysis methods were used to interpret the function of candidate biomarkers in GC. A total of 29 modules were identified via the average linkage hierarchical clustering. A significant module consisting of 48 genes associated with clinical traits was found; three genes with high connectivity in the clinical significant module were identified as hub genes. Among them, SLC5A6 and microfibril‑associated protein 2 (MFAP2) were negatively associated with the overall survival, and their expression was elevated in GC compared with non‑tumor tissues. Additionally, ROC curves indicated that SLC5A6 and MFAP2 showed a good diagnostic power in discriminating cancerous from normal tissues. SLC5A6 and MFAP2 were identified as novel diagnostic and prognostic biomarkers in GC patients; both of these genes were first reported here in connection with GC and deserved further research.

Differential regulatory network-based quantification and prioritization of key genes underlying cancer drug resistance based on time-course RNA-seq data

Drug resistance is a major cause for the failure of cancer chemotherapy or targeted therapy. However, the molecular regulatory mechanisms controlling the dynamic evolvement of drug resistance remain poorly understood. Thus, it is important to develop methods for identifying key gene regulatory mechanisms of the resistance to specific drugs. In this study, we developed a data-driven computational framework, DryNetMC, using a differential regulatory network-based modeling and characterization strategy to quantify and prioritize key genes underlying cancer drug resistance. The DryNetMC does not only infer gene regulatory networks (GRNs) via an integrated approach, but also characterizes and quantifies dynamical network properties for measuring node importance. We used time-course RNA-seq data from glioma cells treated with dbcAMP (a cAMP activator) as a realistic case to reconstruct the GRNs for sensitive and resistant cells. Based on a novel node importance index that comprehensively quantifies network topology, network entropy and expression dynamics, the top ranked genes were verified to be predictive of the drug sensitivities of different glioma cell lines, in comparison with other existing methods. The proposed method provides a quantitative approach to gain insights into the dynamic adaptation and regulatory mechanisms of cancer drug resistance and sheds light on the design of novel biomarkers or targets for predicting or overcoming drug resistance.

Single Cell Gene Co-Expression Network Reveals FECH/CROT Signature as a Prognostic Marker

Aberrant activation of signaling pathways is frequently observed and reported to be associated with the progression and poor prognosis of prostate cancer (PCa). We aimed to identify key biological processes regulated by androgen receptor (AR) using gene co-expression network from single cell resolution. The bimodal index was used to evaluate whether two subpopulations exist among the single cells. Gene expression among single cells revealed averaging pitfalls and bimodality pattern. Weighted gene co-expression network analysis (WGCNA) was used to identify modules of highly correlated genes. Twenty-nine gene modules were identified and AR-regulated modules were screened by significantly overlapping reported androgen induced differentially expressed genes. The biological function "generation of precursor metabolites and energy" was significantly enriched by AR-regulated modules with bimodality, presenting differential androgen response among subpopulations. Integrating with public ChIP-seq data, two genes FECH, and CROT has AR binding sites. Public in vitro studies also show that androgen regulates FECH and CROT. After receiving androgen deprivation therapy, patients lowly express FECH and CROT. Further survival analysis indicates that FECH/CROT signature can predict PCa recurrence. We reveal the heterogeneous function of "generation of precursor metabolites and energy" upon androgen stimulation from the perspective of single cells. Inhibitors targeting this biological process will facilitate to prevent prostate cancer progression.

Identification of an oncogenic network with prognostic and therapeutic value in prostate cancer

Identifying critical pathways governing disease progression is essential for accurate prognosis and effective therapy. We developed a broadly applicable and novel systems‐level gene discovery strategy. This approach focused on constitutively active androgen receptor (AR) splice variant‐driven pathways as representative of an intractable mechanism of prostate cancer (PC) therapeutic resistance. We performed a meta‐analysis of human prostate samples using weighted gene co‐expression network analysis combined with experimental AR variant transcriptome analyses. An AR variant‐driven gene module that is upregulated during human PC progression was identified. We filtered this module by identifying genes that functionally interacted with AR variants using a high‐throughput synthetic genetic array screen in Schizosaccharomyces pombe. This strategy identified seven AR variant‐regulated genes that also enhance AR activity and drive cancer progression. Expression of the seven genes predicted poor disease‐free survival in large independent PC patient cohorts. Pharmacologic inhibition of interacting members of the gene set potently and synergistically decreased PC cell proliferation. This unbiased and novel gene discovery strategy identified a clinically relevant, oncogenic, interacting gene hub with strong prognostic and therapeutic potential in PC.